Iterating over multiple refutation tests

The objective of this notebook is to compare the ability of refuters to detect the problems in a given set of estimators. Note: This notebook makes use of the optional dependencies: - pygraphviz - causalml

Import Dependencies

from dowhy.datasets import linear_dataset
from dowhy import CausalModel
import causalml

Inspection Parameters

These parameters give us the option of inspecting the intermediate steps to sanity check the steps performed

inspect_datasets = True
inspect_models = True
inspect_identified_estimands = True
inspect_estimates = True
inspect_refutations = True

Estimator List

We pass a list of strings, corresponding to the estimators of interest

estimator_list = ["backdoor.propensity_score_matching", "backdoor.propensity_score_weighting", "backdoor.causalml.inference.meta.LRSRegressor"]
method_params= [ None, None, { "init_params":{} } ]

Refuter List

A list of strings, corresponding to each refuter we wish to run

refuter_list = ["bootstrap_refuter", "data_subset_refuter"]

Create the Datasets

# Parameters for creating the Dataset
TREATMENT_IS_BINARY = True
BETA = 10
NUM_SAMPLES = 5000
NUM_CONFOUNDERS = 5
NUM_INSTRUMENTS = 3
NUM_EFFECT_MODIFIERS = 2

# Creating a Linear Dataset with the given parameters
linear_data = linear_dataset(
            beta = BETA,
            num_common_causes = NUM_CONFOUNDERS,
            num_instruments = NUM_INSTRUMENTS,
            num_effect_modifiers = NUM_EFFECT_MODIFIERS,
            num_samples = NUM_SAMPLES,
            treatment_is_binary = True
        )
# Other datasets come here


# Append them together in an array
datasets = [linear_data]

Inspect Data

dataset_num = 1
if inspect_datasets is True:
    for data in datasets:
        print("####### Dataset {}###########################################################################################".format(dataset_num))
        print(data['df'].head())
        print("#############################################################################################################")
        dataset_num += 1

####### Dataset 1###########################################################################################
         X0        X1   Z0        Z1   Z2        W0        W1        W2  \
0 -1.372310 -0.633368  0.0  0.353144  0.0 -0.089772 -0.838580  0.284335
1  0.713931  0.802709  0.0  0.653470  1.0 -1.083905 -1.160895  0.591947
2 -0.384362 -0.451062  0.0  0.341027  1.0 -1.344159 -0.966449 -0.181318
3  1.515319 -1.363540  0.0  0.835704  1.0  0.342380 -1.004874  1.552707
4  0.368875 -0.829938  0.0  0.071521  0.0 -0.128446 -1.994744  0.519826

         W3        W4     v0          y
0  1.223258 -1.124599   True   2.034000
1  1.779737 -0.612423   True  11.125450
2 -0.290884 -1.743305  False  -9.777346
3  1.042000 -2.330312   True  10.343818
4  0.479455 -1.345510  False  -4.253140
#############################################################################################################

Create the CausalModels

models = []
for data in datasets:
    model = CausalModel(
                data = data['df'],
                treatment = data['treatment_name'],
                outcome = data['outcome_name'],
                graph = data['gml_graph']
            )
    models.append(model)

INFO:dowhy.causal_model:Model to find the causal effect of treatment ['v0'] on outcome ['y']

Inspect Models

model_num = 1
if inspect_models is True:
    for model in models:
        print("####### Model {}#############################################################################################".format(model_num))
        print("Common Causes:",model._common_causes)
        print("Effect Modifiers:",model._effect_modifiers)
        print("Instruments:",model._instruments)
        print("Outcome:",model._outcome)
        print("Treatment:",model._treatment)
        print("#############################################################################################################")
        model_num += 1

####### Model 1#############################################################################################
Common Causes: ['W4', 'W2', 'W1', 'W3', 'W0', 'Unobserved Confounders']
Effect Modifiers: ['X0', 'X1']
Instruments: ['Z2', 'Z1', 'Z0']
Outcome: ['y']
Treatment: ['v0']
#############################################################################################################

Identify Effect

identified_estimands = []
for model in models:
    identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)
    identified_estimands.append(identified_estimand)

WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.
INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.
INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:['Z2', 'Z1', 'Z0']
INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:[]

Identified Estimands

estimand_count = 1
for estimand in identified_estimands:
    print("####### Identified Estimand {}#####################################################################################".format(estimand_count))
    print(estimand)
    print("###################################################################################################################")
    estimand_count += 1

####### Identified Estimand 1#####################################################################################
Estimand type: nonparametric-ate

### Estimand : 1
Estimand name: backdoor1
Estimand expression:
  d
─────(Expectation(y|W4,W2,W1,W3,W0))
d[v₀]
Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W1,W3,W0,U) = P(y|v0,W4,W2,W1,W3,W0)

### Estimand : 2
Estimand name: backdoor2
Estimand expression:
  d
─────(Expectation(y|W4,W2,W1,W3,W0,X1))
d[v₀]
Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W1,W3,W0,X1,U) = P(y|v0,W4,W2,W1,W3,W0,X1)

### Estimand : 3
Estimand name: backdoor3
Estimand expression:
  d
─────(Expectation(y|W4,W2,W1,W3,W0,X0))
d[v₀]
Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W1,W3,W0,X0,U) = P(y|v0,W4,W2,W1,W3,W0,X0)

### Estimand : 4
Estimand name: backdoor4 (Default)
Estimand expression:
  d
─────(Expectation(y|W4,W2,W1,W3,W0,X1,X0))
d[v₀]
Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W1,W3,W0,X1,X0,U) = P(y|v0,W4,W2,W1,W3,W0,X1,X0)

### Estimand : 5
Estimand name: iv
Estimand expression:
Expectation(Derivative(y, [Z2, Z1, Z0])*Derivative([v0], [Z2, Z1, Z0])**(-1))
Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z2,Z1,Z0})
Estimand assumption 2, Exclusion: If we remove {Z2,Z1,Z0}→{v0}, then ¬({Z2,Z1,Z0}→y)

### Estimand : 6
Estimand name: frontdoor
No such variable found!

###################################################################################################################

Estimate Effect

estimate_list = []
for i in range(len(identified_estimands)):
    for j in range(len(estimator_list)):
        estimate = model.estimate_effect(
                        identified_estimands[i],
                        method_name=estimator_list[j],
                        method_params=method_params[j]
                  )
        estimate_list.append(estimate)

INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
/home/amit/py-envs/env3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  return f(**kwargs)
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
/home/amit/py-envs/env3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  return f(**kwargs)
The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0464
INFO:causalml:    RMSE (Treatment):     0.7089
INFO:causalml:   sMAPE   (Control):     0.5415
INFO:causalml:   sMAPE (Treatment):     0.1450
INFO:causalml:    Gini   (Control):     0.7392
INFO:causalml:    Gini (Treatment):     0.9950

{'X':            W4        W2        W1        W3        W0        X1        X0  \
0   -1.124599  0.284335 -0.838580  1.223258 -0.089772 -0.633368 -1.372310
1   -0.612423  0.591947 -1.160895  1.779737 -1.083905  0.802709  0.713931
2   -1.743305 -0.181318 -0.966449 -0.290884 -1.344159 -0.451062 -0.384362
3   -2.330312  1.552707 -1.004874  1.042000  0.342380 -1.363540  1.515319
4   -1.345510  0.519826 -1.994744  0.479455 -0.128446 -0.829938  0.368875
..        ...       ...       ...       ...       ...       ...       ...
995 -2.515948  0.660984 -1.169142 -2.272146 -1.210642 -0.284190 -0.127452
996 -2.943305  1.765768 -0.453093  1.811460  1.207236  1.056658  2.059214
997 -0.903931 -0.733567 -0.631920  2.074003 -1.104834 -0.227741 -0.262214
998 -0.107884  0.866378 -1.644002 -0.046340 -1.318515  0.888535  1.662692
999 -1.664178  0.364437 -0.018848  0.440983 -1.575637 -0.289049 -0.646924

           X0        X1
0   -1.372310 -0.633368
1    0.713931  0.802709
2   -0.384362 -0.451062
3    1.515319 -1.363540
4    0.368875 -0.829938
..        ...       ...
995 -0.127452 -0.284190
996  2.059214  1.056658
997 -0.262214 -0.227741
998  1.662692  0.888535
999 -0.646924 -0.289049

[1000 rows x 9 columns], 'y': 0       2.034000
1      11.125450
2      -9.777346
3      10.343818
4      -4.253140
         ...
995   -13.283829
996    17.211685
997     4.590505
998    13.321122
999    -8.100697
Name: y, Length: 1000, dtype: float64, 'treatment': 0       True
1       True
2      False
3       True
4      False
       ...
995    False
996     True
997     True
998     True
999    False
Name: v0, Length: 1000, dtype: bool}

Estimate Values

estimand_count = 1
if inspect_estimates is True:
    for estimand in estimate_list:
        print("####### Estimand {}#######################################################################################".format(estimand_count))
        print("*** Class Name ***")
        print()
        print(estimand.params['estimator_class'])
        print()
        print(estimand)
        print("########################################################################################################")
        print()
        estimand_count += 1

####### Estimand 1#######################################################################################
*** Class Name ***

<class 'dowhy.causal_estimators.propensity_score_matching_estimator.PropensityScoreMatchingEstimator'>

*** Causal Estimate ***

## Identified estimand
Estimand type: nonparametric-ate

## Realized estimand
b: y~v0+W4+W2+W1+W3+W0+X1+X0
Target units: ate

## Estimate
Mean value: 12.89367255934708

########################################################################################################

####### Estimand 2#######################################################################################
*** Class Name ***

<class 'dowhy.causal_estimators.propensity_score_weighting_estimator.PropensityScoreWeightingEstimator'>

*** Causal Estimate ***

## Identified estimand
Estimand type: nonparametric-ate

## Realized estimand
b: y~v0+W4+W2+W1+W3+W0+X1+X0
Target units: ate

## Estimate
Mean value: 12.075122341243496

########################################################################################################

####### Estimand 3#######################################################################################
*** Class Name ***

<class 'dowhy.causal_estimators.causalml.Causalml'>

*** Causal Estimate ***

## Identified estimand
Estimand type: nonparametric-ate

## Realized estimand
b: y~v0+W4+W2+W1+W3+W0+X1+X0
Target units: ate

## Estimate
Mean value: [11.29290641]

########################################################################################################

Refute Estimate

refutation_list = []
for estimand in identified_estimands:
    for estimate in estimate_list:
        for refuter in refuter_list:
            ref = model.refute_estimate(estimand, estimate,method_name=refuter)
            refutation_list.append(ref)

INFO:dowhy.causal_refuters.bootstrap_refuter:All variables required: Running bootstrap adding noise to confounders, instrumental variables and effect modifiers.
INFO:dowhy.causal_refuters.bootstrap_refuter:INFO: The chosen variables are: W4,W2,W1,W3,W0,X1,X0,Z2,Z1,Z0,X0,X1
INFO:dowhy.causal_refuters.bootstrap_refuter:Refutation over 100 simulated datasets of size 1000 each
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_refuters.bootstrap_refuter:Making use of Bootstrap as we have more than 100 examples.
                 Note: The greater the number of examples, the more accurate are the confidence estimates
INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.8 simulated datasets of size 800.0 each
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_refuters.data_subset_refuter:Making use of Bootstrap as we have more than 100 examples.
                 Note: The greater the number of examples, the more accurate are the confidence estimates
INFO:dowhy.causal_refuters.bootstrap_refuter:All variables required: Running bootstrap adding noise to confounders, instrumental variables and effect modifiers.
INFO:dowhy.causal_refuters.bootstrap_refuter:INFO: The chosen variables are: W4,W2,W1,W3,W0,X1,X0,Z2,Z1,Z0,X0,X1
INFO:dowhy.causal_refuters.bootstrap_refuter:Refutation over 100 simulated datasets of size 1000 each
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_refuters.bootstrap_refuter:Making use of Bootstrap as we have more than 100 examples.
                 Note: The greater the number of examples, the more accurate are the confidence estimates
INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.8 simulated datasets of size 800.0 each
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
INFO:dowhy.causal_refuters.data_subset_refuter:Making use of Bootstrap as we have more than 100 examples.
                 Note: The greater the number of examples, the more accurate are the confidence estimates
INFO:dowhy.causal_refuters.bootstrap_refuter:All variables required: Running bootstrap adding noise to confounders, instrumental variables and effect modifiers.
INFO:dowhy.causal_refuters.bootstrap_refuter:INFO: The chosen variables are: W4,W2,W1,W3,W0,X1,X0,Z2,Z1,Z0,X0,X1
INFO:dowhy.causal_refuters.bootstrap_refuter:Refutation over 100 simulated datasets of size 1000 each
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9200
INFO:causalml:    RMSE (Treatment):     0.9412
INFO:causalml:   sMAPE   (Control):     0.4925
INFO:causalml:   sMAPE (Treatment):     0.1825
INFO:causalml:    Gini   (Control):     0.7570
INFO:causalml:    Gini (Treatment):     0.9895

{'X':            W4        W2        W1        W3        W0        X1        X0  \
283 -2.374933  0.728965 -0.368648 -0.606642  1.295353 -1.524890  0.835089
817  0.427994  1.189968 -0.603155 -0.053104  0.380095 -0.564310  1.043702
433 -1.848292  1.925231 -0.746389  0.838242  0.454850 -1.782858  0.315274
518 -1.612950 -0.302839 -2.806152  2.278601  0.851459 -0.216496 -0.266022
760 -1.603621  0.332341  0.905154 -1.867772 -0.335681 -1.016100  0.900808
..        ...       ...       ...       ...       ...       ...       ...
660 -1.355739  1.511612  0.382854 -1.674767  1.278216  0.055724  0.586001
983 -1.218331  1.260066 -0.595561 -0.288691  0.395709 -0.170658 -0.066706
98  -0.845113 -0.187431 -2.262896  1.378585 -0.644947  1.959020  0.299983
972 -0.081695  0.594733 -0.969979  1.791205  1.145891 -1.370514 -1.418567
923 -1.936169  2.043045 -0.395622  2.093163 -0.270379 -1.179542  1.467835

           X0        X1
283  0.835089 -1.524890
817  1.043702 -0.564310
433  0.315274 -1.782858
518 -0.266022 -0.216496
760  0.900808 -1.016100
..        ...       ...
660  0.586001  0.055724
983 -0.066706 -0.170658
98   0.299983  1.959020
972 -1.418567 -1.370514
923  1.467835 -1.179542

[1000 rows x 9 columns], 'y': 283     7.081134
817    17.203287
433     7.710126
518    -0.840870
760    -7.109575
         ...
660    11.491007
983     7.361146
98     -3.658929
972    10.333966
923    11.892142
Name: y, Length: 1000, dtype: float64, 'treatment': 283     True
817     True
433     True
518    False
760    False
       ...
660     True
983     True
98     False
972     True
923     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8989
INFO:causalml:    RMSE (Treatment):     0.9262
INFO:causalml:   sMAPE   (Control):     0.5243
INFO:causalml:   sMAPE (Treatment):     0.1639
INFO:causalml:    Gini   (Control):     0.7420
INFO:causalml:    Gini (Treatment):     0.9882
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
583 -0.908339 -0.943344 -1.576363 -0.129984  0.644029 -0.283406  1.047899
894 -0.035766  0.592029 -2.679506  0.298057 -0.918355 -0.136446  1.547318
441  0.082396  1.734068 -1.234019 -1.393834 -0.012471  0.176847  2.271376
19  -0.501483 -0.141436 -0.783533  2.196045 -1.062579 -1.248909  0.217419
635 -0.328230  2.032342 -1.397294  1.604553 -0.906293  1.272577  1.348827
..        ...       ...       ...       ...       ...       ...       ...
681 -0.659808  1.173054  0.680265 -1.119354  1.271902 -0.028103  1.906201
999 -1.548070  0.488709  0.070530  0.535239 -1.520216 -0.223398 -0.643285
601  0.197730 -0.348816 -2.289017  0.706303 -0.436227 -0.869317  0.393254
922 -0.957356  2.303012  1.357823 -2.150545 -0.526943  0.693399 -0.542549
85  -1.507585  1.395092 -0.629172 -0.127097 -1.095902 -2.763081 -0.159951

           X0        X1
583  1.047899 -0.283406
894  1.547318 -0.136446
441  2.271376  0.176847
19   0.217419 -1.248909
635  1.348827  1.272577
..        ...       ...
681  1.906201 -0.028103
999 -0.643285 -0.223398
601  0.393254 -0.869317
922 -0.542549  0.693399
85  -0.159951 -2.763081

[1000 rows x 9 columns], 'y': 583    -2.819370
894    -2.776312
441    18.248395
19      6.700542
635    15.613398
         ...
681    18.090454
999    -8.100697
601    -1.707354
922     5.614218
85     -0.384362
Name: y, Length: 1000, dtype: float64, 'treatment': 583    False
894    False
441     True
19      True
635     True
       ...
681     True
999    False
601    False
922     True
85      True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0143
INFO:causalml:    RMSE (Treatment):     0.9978
INFO:causalml:   sMAPE   (Control):     0.5401
INFO:causalml:   sMAPE (Treatment):     0.1764
INFO:causalml:    Gini   (Control):     0.7350
INFO:causalml:    Gini (Treatment):     0.9868

{'X':            W4        W2        W1        W3        W0        X1        X0  \
46  -0.811883  1.026880 -0.632654  0.186775 -1.837921  0.413697  1.853316
438 -1.340902  0.995483 -0.161424  0.746464 -0.289784 -0.655573  0.779645
658 -0.748875  0.200811 -2.089700  0.715665 -0.392931 -0.898996  0.666150
789 -1.509320 -0.240769  1.557885 -0.108347 -0.045226  0.412988  1.082957
702  1.732221  1.344143  0.218604 -0.185893  1.519994  0.269678  0.498933
..        ...       ...       ...       ...       ...       ...       ...
351 -0.831764  0.623752 -0.471018  0.970281 -0.177779 -1.707145  0.027269
513 -0.705018  1.506675 -0.629371  2.376489  3.098338 -0.308193  0.523668
784 -1.254205  0.728475 -1.482794 -0.023014 -0.163497  2.000347  1.558882
631  0.369918  2.644170 -0.867136  1.586151 -0.202800 -0.997252  0.248708
469 -1.874152  0.163053  0.352744 -2.044516 -1.017211 -1.678673  1.633251

           X0        X1
46   1.853316  0.413697
438  0.779645 -0.655573
658  0.666150 -0.898996
789  1.082957  0.412988
702  0.498933  0.269678
..        ...       ...
351  0.027269 -1.707145
513  0.523668 -0.308193
784  1.558882  2.000347
631  0.248708 -0.997252
469  1.633251 -1.678673

[1000 rows x 9 columns], 'y': 46     -6.116730
438     8.571639
658     8.483868
789    11.103887
702    23.877017
         ...
351     7.214058
513    22.411994
784    14.827481
631    14.220450
469     4.435916
Name: y, Length: 1000, dtype: float64, 'treatment': 46     False
438     True
658     True
789     True
702     True
       ...
351     True
513     True
784     True
631     True
469     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8926
INFO:causalml:    RMSE (Treatment):     0.9835
INFO:causalml:   sMAPE   (Control):     0.5059
INFO:causalml:   sMAPE (Treatment):     0.1802
INFO:causalml:    Gini   (Control):     0.7225
INFO:causalml:    Gini (Treatment):     0.9871
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
110 -1.701609  2.531055 -0.293499  1.383457 -0.385890  1.021554  0.634298
973  0.497997  0.870619 -1.399658  0.868762  1.005668 -1.759774 -1.487125
253  0.008862  0.472530 -0.278656  1.597479 -1.854917  0.218323  1.158774
774 -0.690558  0.152730  0.466841 -0.784936  0.262644 -1.272747  0.138279
25  -0.555348  1.680622 -0.274376  0.085447 -1.937412 -0.524363  1.456005
..        ...       ...       ...       ...       ...       ...       ...
433 -1.759517  1.932299 -0.786689  0.690382  0.385271 -1.815584  0.191754
517 -1.412816  0.065002  0.010819  1.228889 -1.019312 -0.737759  1.051565
241 -0.690211  2.358994 -0.801637  1.877993  0.244108 -2.726661 -0.526157
840  0.825515  0.764723  0.303671  0.981057 -0.217922 -0.091909  0.598068
808 -0.535757  0.997121 -0.516028 -0.574787  0.787579 -1.200082  1.220149

           X0        X1
110  0.634298  1.021554
973 -1.487125 -1.759774
253  1.158774  0.218323
774  0.138279 -1.272747
25   1.456005 -0.524363
..        ...       ...
433  0.191754 -1.815584
517  1.051565 -0.737759
241 -0.526157 -2.726661
840  0.598068 -0.091909
808  1.220149 -1.200082

[1000 rows x 9 columns], 'y': 110    11.934711
973     8.500263
253    11.727481
774     6.080336
25      8.623876
         ...
433     7.710126
517    -4.981074
241     7.793042
840    15.748095
808    14.588358
Name: y, Length: 1000, dtype: float64, 'treatment': 110     True
973     True
253     True
774     True
25      True
       ...
433     True
517    False
241     True
840     True
808     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
553 -0.320702  2.050546  0.848221  0.065701  0.516036 -0.115430 -0.448765
263 -0.436130  1.311757  0.801788  0.976110 -1.434723  1.339524 -0.740828
318 -0.323014 -0.607556 -0.157762  0.004904  1.536271 -0.407724 -0.367022
106 -1.558304  1.265530  0.118377 -0.333680 -1.996853 -1.773391  1.138244
926  1.123598  0.627467 -0.761817  1.094964 -0.861427 -0.395120  0.162772
..        ...       ...       ...       ...       ...       ...       ...
354 -2.470658  2.299726 -0.513947 -0.533862  1.235853  0.471081  0.403984
665 -2.051712  0.111099 -0.161581 -0.859187  0.133294 -0.468604 -0.057014
43  -0.942660  1.261453  0.077870  0.553020 -1.012981 -0.392755  1.398809
805 -2.534599 -0.173973 -0.855050 -0.359868  0.531906 -2.824743  0.317926
873 -0.107712 -0.360830  0.754699  0.645171 -1.063732 -2.205695  0.040772

           X0        X1
553 -0.448765 -0.115430
263 -0.740828  1.339524
318 -0.367022 -0.407724
106  1.138244 -1.773391
926  0.162772 -0.395120
..        ...       ...
354  0.403984  0.471081
665 -0.057014 -0.468604
43   1.398809 -0.392755
805  0.317926 -2.824743
873  0.040772 -2.205695

[1000 rows x 9 columns], 'y': 553    11.754777
263     5.315574
318    10.068543
106    -8.631314
926    13.032000
         ...
354     9.572774
665     2.331887
43     11.590880
805    -0.721618
873     4.499265
Name: y, Length: 1000, dtype: float64, 'treatment': 553     True
263     True
318     True
106    False
926     True
       ...
354     True
665     True
43      True
805     True
873     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1093
INFO:causalml:    RMSE (Treatment):     1.0340
INFO:causalml:   sMAPE   (Control):     0.5506
INFO:causalml:   sMAPE (Treatment):     0.1787
INFO:causalml:    Gini   (Control):     0.7321
INFO:causalml:    Gini (Treatment):     0.9865
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9188
INFO:causalml:    RMSE (Treatment):     0.9924
INFO:causalml:   sMAPE   (Control):     0.5366
INFO:causalml:   sMAPE (Treatment):     0.1953
INFO:causalml:    Gini   (Control):     0.7756
INFO:causalml:    Gini (Treatment):     0.9901
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
615 -1.406038  0.572876 -0.698405  1.326372 -2.542542 -1.169138  2.178432
124 -1.122468  2.232510  0.453520  0.286994  0.573583 -0.104966 -0.616633
671 -1.835079  1.952091  0.539765  0.587736 -1.553163 -0.139247 -0.565739
354 -2.414675  2.195083 -0.552665 -0.286460  1.364807  0.672503  0.211691
971 -1.008125  0.813609 -1.174516  0.831382  0.600010  1.860670  1.222419
..        ...       ...       ...       ...       ...       ...       ...
140 -0.076572 -0.183218 -1.087970  1.721934 -1.409678  0.371262  0.084339
441  0.164827  1.727422 -1.133955 -1.106994 -0.174687  0.462557  2.301826
944 -1.317575  1.334951  0.214215 -0.090740  0.344393 -1.939722  0.185537
504 -1.936715  2.198026  0.936412 -0.674665 -1.200826 -0.802310  0.450366
579 -0.367602  0.368260  0.366198  0.370464 -1.268264 -1.013708 -0.296588

           X0        X1
615  2.178432 -1.169138
124 -0.616633 -0.104966
671 -0.565739 -0.139247
354  0.211691  0.672503
971  1.222419  1.860670
..        ...       ...
140  0.084339  0.371262
441  2.301826  0.462557
944  0.185537 -1.939722
504  0.450366 -0.802310
579 -0.296588 -1.013708

[1000 rows x 9 columns], 'y': 615    -8.516564
124    10.147994
671    -6.150284
354     9.572774
971    16.046613
         ...
140    -3.623800
441    18.248395
944     5.893961
504     4.307409
579     3.518524
Name: y, Length: 1000, dtype: float64, 'treatment': 615    False
124     True
671    False
354     True
971     True
       ...
140    False
441     True
944     True
504     True
579     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1321
INFO:causalml:    RMSE (Treatment):     0.9716
INFO:causalml:   sMAPE   (Control):     0.5822
INFO:causalml:   sMAPE (Treatment):     0.1791
INFO:causalml:    Gini   (Control):     0.6922
INFO:causalml:    Gini (Treatment):     0.9885

{'X':            W4        W2        W1        W3        W0        X1        X0  \
483 -1.202986  0.901468 -0.302848  0.668258 -1.940498  0.222960  0.612932
778 -2.032255 -0.391939  0.024425  0.116800  0.320421 -1.642899  0.789443
364 -1.123898  1.426482  1.582027 -0.519401 -1.549683 -0.385146  0.951193
437 -1.765053  2.519452  0.710983  0.111217 -1.534482 -1.368957 -0.086755
518 -1.577778 -0.426756 -2.600338  2.425733  0.913174  0.100396 -0.440147
..        ...       ...       ...       ...       ...       ...       ...
414 -0.773099  0.363187 -2.923567  0.521046 -1.501182 -1.106220  2.408646
419 -0.132182 -0.589329  0.304130  0.361943 -0.778306  1.341490  0.877095
971 -1.156021  0.689227 -1.100481  1.059006  0.528747  1.847563  1.274255
855 -1.604108  2.748376  0.301092 -0.817147 -0.256705 -0.736611  0.902897
971 -1.047919  0.711559 -1.179255  1.142261  0.330883  1.845635  1.100617

           X0        X1
483  0.612932  0.222960
778  0.789443 -1.642899
364  0.951193 -0.385146
437 -0.086755 -1.368957
518 -0.440147  0.100396
..        ...       ...
414  2.408646 -1.106220
419  0.877095  1.341490
971  1.274255  1.847563
855  0.902897 -0.736611
971  1.100617  1.845635

[1000 rows x 9 columns], 'y': 483     5.198246
778     6.019405
364     7.693260
437     1.751543
518    -0.840870
         ...
414    -6.908901
419    12.060932
971    16.046613
855     8.939223
971    16.046613
Name: y, Length: 1000, dtype: float64, 'treatment': 483     True
778     True
364     True
437     True
518    False
       ...
414    False
419     True
971     True
855     True
971     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9750
INFO:causalml:    RMSE (Treatment):     0.9816
INFO:causalml:   sMAPE   (Control):     0.4629
INFO:causalml:   sMAPE (Treatment):     0.1647
INFO:causalml:    Gini   (Control):     0.7145
INFO:causalml:    Gini (Treatment):     0.9878

{'X':            W4        W2        W1        W3        W0        X1        X0  \
257 -2.430668  2.899099 -1.685012  0.871124 -0.830481  0.568640 -0.719302
190 -1.027333  0.125718 -0.288925  0.143558 -1.665370 -1.891319  1.401234
608  0.582419  2.904835 -1.679491 -1.433618  0.523356  1.427560 -0.365038
136 -1.844962 -0.175424 -1.127409  1.046034 -0.143139  0.929437 -0.113900
698 -1.136466  1.658704  0.342969  1.270983 -1.275504  0.318959  0.063527
..        ...       ...       ...       ...       ...       ...       ...
2   -1.717141 -0.110696 -0.851043 -0.159615 -1.330690 -0.575017  0.179750
525 -1.122974  0.528479 -0.843057  0.862124  0.326137 -0.908267  0.675998
818 -2.224628  1.267784 -0.121270  0.339637 -2.509032 -0.733553  0.969607
772 -1.528754  0.577981 -1.230741  1.515439 -0.409506 -1.244127 -0.229884
725 -1.062839  1.197021 -2.332341  1.110674 -0.732020  1.117293 -1.286959

           X0        X1
257 -0.719302  0.568640
190  1.401234 -1.891319
608 -0.365038  1.427560
136 -0.113900  0.929437
698  0.063527  0.318959
..        ...       ...
2    0.179750 -0.575017
525  0.675998 -0.908267
818  0.969607 -0.733553
772 -0.229884 -1.244127
725 -1.286959  1.117293

[1000 rows x 9 columns], 'y': 257     2.568588
190     3.713965
608    13.248344
136    -4.977571
698     7.381116
         ...
2      -9.777346
525     9.406466
818   -11.770683
772     3.250030
725    -3.118501
Name: y, Length: 1000, dtype: float64, 'treatment': 257     True
190     True
608     True
136    False
698     True
       ...
2      False
525     True
818    False
772     True
725    False
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0590
INFO:causalml:    RMSE (Treatment):     1.0471
INFO:causalml:   sMAPE   (Control):     0.5733
INFO:causalml:   sMAPE (Treatment):     0.1911
INFO:causalml:    Gini   (Control):     0.7229
INFO:causalml:    Gini (Treatment):     0.9862
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
277 -0.818334  0.467892  0.008818 -0.768910 -0.762255 -2.037111 -0.267297
616 -1.801324  0.465489  1.310050  0.431569 -0.724100  0.078453 -0.281507
251 -2.045885 -0.672299  0.461639  1.102677 -0.212650 -0.728915  0.412898
779 -1.293648  0.585981  0.057450  0.159790  0.220197 -0.968476 -0.039945
891 -0.173672 -0.339079 -1.162503  1.699004  1.016864  0.402539  1.951243
..        ...       ...       ...       ...       ...       ...       ...
220  0.682316  1.258320 -0.491097  0.533450 -1.171028 -1.744354  0.764456
880 -1.819713  1.795421  2.289274 -0.412771  0.834959 -0.625579  0.310392
600 -2.029851  0.999468 -0.650238  0.199650 -1.522578 -0.197616 -0.487230
85  -1.689351  1.444803 -0.346028 -0.249449 -0.882348 -2.851097 -0.324837
982 -0.727784  0.731025 -0.402121 -0.987436  0.733502 -1.450469  1.225178

           X0        X1
277 -0.267297 -2.037111
616 -0.281507  0.078453
251  0.412898 -0.728915
779 -0.039945 -0.968476
891  1.951243  0.402539
..        ...       ...
220  0.764456 -1.744354
880  0.310392 -0.625579
600 -0.487230 -0.197616
85  -0.324837 -2.851097
982  1.225178 -1.450469

[1000 rows x 9 columns], 'y': 277     1.863805
616     4.604313
251     5.436045
779     7.001850
891    20.166272
         ...
220    10.878908
880    10.445032
600    -2.038553
85     -0.384362
982    12.003112
Name: y, Length: 1000, dtype: float64, 'treatment': 277    True
616    True
251    True
779    True
891    True
       ...
220    True
880    True
600    True
85     True
982    True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.6412
INFO:causalml:    RMSE (Treatment):     0.8491
INFO:causalml:   sMAPE   (Control):     0.4837
INFO:causalml:   sMAPE (Treatment):     0.1494
INFO:causalml:    Gini   (Control):     0.7786
INFO:causalml:    Gini (Treatment):     0.9905

{'X':            W4        W2        W1        W3        W0        X1        X0  \
241 -0.845996  2.158548 -0.696759  1.834804  0.107037 -2.852733 -0.048085
896 -2.469059 -0.770860  1.142546  0.461676 -1.314687 -0.322351 -1.475853
723 -0.193634  1.302947  0.686441  0.892449 -0.623708 -0.382725  0.546637
758 -0.229260  0.153205 -0.797357  0.977691 -0.149110 -1.096648  1.138511
649 -2.097121  1.286213 -0.793670  1.771977 -0.505928 -0.172366  1.686361
..        ...       ...       ...       ...       ...       ...       ...
177 -1.890133  0.349791  2.039631  0.747699  0.029708 -1.310446  2.620265
265 -0.579746  2.246286  0.968432 -1.288886 -0.003273 -0.503409  0.023601
786  1.367241  2.337107 -1.382961  0.925568 -1.373163 -0.581355 -1.057320
563 -0.487488 -0.303342 -2.960748  1.385569 -1.367310  1.270372  0.489614
145 -2.808225  1.476268 -0.669289  0.837867  0.318861  0.101554 -0.005306

           X0        X1
241 -0.048085 -2.852733
896 -1.475853 -0.322351
723  0.546637 -0.382725
758  1.138511 -1.096648
649  1.686361 -0.172366
..        ...       ...
177  2.620265 -1.310446
265  0.023601 -0.503409
786 -1.057320 -0.581355
563  0.489614  1.270372
145 -0.005306  0.101554

[1000 rows x 9 columns], 'y': 241     7.793042
896    -7.004058
723    12.592456
758    12.802985
649    11.515556
         ...
177    13.887378
265     9.003069
786     8.954078
563    -5.142521
145    -4.715614
Name: y, Length: 1000, dtype: float64, 'treatment': 241     True
896     True
723     True
758     True
649     True
       ...
177     True
265     True
786     True
563    False
145    False
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9464
INFO:causalml:    RMSE (Treatment):     0.9755
INFO:causalml:   sMAPE   (Control):     0.4851
INFO:causalml:   sMAPE (Treatment):     0.1754
INFO:causalml:    Gini   (Control):     0.7484
INFO:causalml:    Gini (Treatment):     0.9879

{'X':            W4        W2        W1        W3        W0        X1        X0  \
303 -1.782573  1.161716 -0.132032  1.247322  0.906861 -0.589598  0.566135
928 -0.082647 -0.854352 -0.877404  0.258306 -0.690152  0.621097 -1.202361
188  1.870551  1.402824 -0.641787  0.898412 -0.955153  0.086009  0.396813
7    0.134461  1.635799 -1.651504  0.903543 -1.832069 -0.771336  1.027382
536 -1.413058  0.331189 -0.549325 -0.161507 -0.050805  1.085822 -1.137920
..        ...       ...       ...       ...       ...       ...       ...
498  1.018865  1.167549  0.300948  0.499916  0.024608 -0.209970  0.662375
42   0.685643  0.464868 -0.530367  1.008595  1.087561  0.792421  0.560773
540 -1.459292  1.994080 -0.337395  0.007646  2.188399  0.628455  1.352454
83  -0.635356 -0.756915  0.860081  0.351036 -0.501081 -0.642911 -0.373147
781 -0.997105  1.206782 -0.466358 -0.047794 -0.294843 -1.540877  0.881991

           X0        X1
303  0.566135 -0.589598
928 -1.202361  0.621097
188  0.396813  0.086009
7    1.027382 -0.771336
536 -1.137920  1.085822
..        ...       ...
498  0.662375 -0.209970
42   0.560773  0.792421
540  1.352454  0.628455
83  -0.373147 -0.642911
781  0.881991 -1.540877

[1000 rows x 9 columns], 'y': 303    11.687300
928     3.466874
188    17.138200
7      -2.646268
536     3.123954
         ...
498    17.581110
42     19.574715
540    19.517995
83      5.781616
781     8.454186
Name: y, Length: 1000, dtype: float64, 'treatment': 303     True
928     True
188     True
7      False
536     True
       ...
498     True
42      True
540     True
83      True
781     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9429
INFO:causalml:    RMSE (Treatment):     0.9998
INFO:causalml:   sMAPE   (Control):     0.5267
INFO:causalml:   sMAPE (Treatment):     0.1899
INFO:causalml:    Gini   (Control):     0.7351
INFO:causalml:    Gini (Treatment):     0.9890
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
955 -0.497739  0.540374 -1.677981  1.349484  2.091278 -1.039961  1.206380
435 -1.256109  1.764761 -0.149741  0.355327 -0.774481  0.614483  1.777844
354 -2.424064  2.357013 -0.394928 -0.458982  1.377004  0.445340  0.305403
251 -1.935402 -0.583126  0.125224  1.325398 -0.135370 -0.313797  0.341698
479 -2.307127  0.566961 -0.554632 -0.132100 -2.127523  0.432313 -0.415788
..        ...       ...       ...       ...       ...       ...       ...
32  -0.982458  0.620985  0.106409 -0.629536 -1.279116  0.532026  0.410600
233  0.083363  1.012340 -0.976858  0.790091  0.179734 -1.685756  3.030718
26  -0.078329  0.136605  0.222189  2.949437 -0.460346 -1.743903  1.989701
379 -2.647414  2.197177 -0.438679 -0.673437  0.049419 -1.671370  0.620090
845 -1.097083 -0.445490 -1.364601  0.971446 -1.841247  0.187582 -0.453287

           X0        X1
955  1.206380 -1.039961
435  1.777844  0.614483
354  0.305403  0.445340
251  0.341698 -0.313797
479 -0.415788  0.432313
..        ...       ...
32   0.410600  0.532026
233  3.030718 -1.685756
26   1.989701 -1.743903
379  0.620090 -1.671370
845 -0.453287  0.187582

[1000 rows x 9 columns], 'y': 955    18.503792
435    13.857254
354     9.572774
251     5.436045
479    -4.199785
         ...
32     -5.941016
233    21.285516
26     17.468518
379    -6.731177
845    -8.309502
Name: y, Length: 1000, dtype: float64, 'treatment': 955     True
435     True
354     True
251     True
479     True
       ...
32     False
233     True
26      True
379    False
845    False
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9546
INFO:causalml:    RMSE (Treatment):     0.8873
INFO:causalml:   sMAPE   (Control):     0.5285
INFO:causalml:   sMAPE (Treatment):     0.1608
INFO:causalml:    Gini   (Control):     0.7781
INFO:causalml:    Gini (Treatment):     0.9905

{'X':            W4        W2        W1        W3        W0        X1        X0  \
137 -1.870239  1.002550 -1.280447  0.096607 -1.693014 -0.218161 -0.988356
265 -0.513343  2.114772  0.996179 -1.443963 -0.114143 -0.406941 -0.045786
777 -0.493441  1.716078  0.792791 -0.043363 -0.556987 -1.268526  1.451878
5    0.081454 -0.180901 -0.354700 -1.459105  0.296656 -0.759733 -0.701632
487 -1.420850  0.398972  1.112433 -0.228356  0.349759 -1.154691 -0.299466
..        ...       ...       ...       ...       ...       ...       ...
467 -1.547984  1.672450  0.493790 -0.006961  1.256418  0.077298  0.376628
118 -1.453238  0.945857  0.236851 -0.637282 -0.686940  0.717364  0.140900
866 -2.650644 -0.029222 -0.964702 -0.443857  0.949448  0.071478  0.590612
776 -2.793850 -0.766315 -0.437998  0.125062 -1.087616  0.492760  1.852926
454 -0.520798  0.402434 -0.807018  0.681877 -1.949777  0.007282  2.327974

           X0        X1
137 -0.988356 -0.218161
265 -0.045786 -0.406941
777  1.451878 -1.268526
5   -0.701632 -0.759733
487 -0.299466 -1.154691
..        ...       ...
467  0.376628  0.077298
118  0.140900  0.717364
866  0.590612  0.071478
776  1.852926  0.492760
454  2.327974  0.007282

[1000 rows x 9 columns], 'y': 137    -9.707122
265     9.003069
777    12.450890
5       4.741680
487     5.416248
         ...
467    12.156180
118     6.682644
866     6.414822
776   -12.340879
454    12.121477
Name: y, Length: 1000, dtype: float64, 'treatment': 137    False
265     True
777     True
5       True
487     True
       ...
467     True
118     True
866     True
776    False
454     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0762
INFO:causalml:    RMSE (Treatment):     0.9945
INFO:causalml:   sMAPE   (Control):     0.5339
INFO:causalml:   sMAPE (Treatment):     0.1711

{'X':            W4        W2        W1        W3        W0        X1        X0  \
829 -1.405635 -0.270187 -1.959880 -0.889368 -1.212302 -1.298171 -0.443938
201 -2.536105  0.014067 -1.257571  0.682911 -1.447216 -1.100839  1.130308
561 -0.529711 -0.174353 -0.254554  0.894866 -2.286815  0.253255  1.165439
370 -1.243201  0.578033 -1.039329  0.953519 -1.614025 -0.207823  0.878261
921 -0.681534  0.706844 -0.752564  0.023439  0.770400 -0.849537  1.956007
..        ...       ...       ...       ...       ...       ...       ...
47   0.236697  1.161300 -1.267837 -1.410815 -0.159012 -1.005670  1.316541
526  1.648612  0.824729 -0.062684  0.566424  0.909295 -0.692820  0.337959
67  -0.705354  0.116565 -0.145444  1.079454 -0.611558 -2.607392 -1.660696
637 -1.335351  0.667491 -0.005216 -1.587485 -2.347921 -1.555458  1.082437
96   1.179559  0.881080 -0.248690  0.957809 -1.554978 -1.030593  1.149389

           X0        X1
829 -0.443938 -1.298171
201  1.130308 -1.100839
561  1.165439  0.253255
370  0.878261 -0.207823
921  1.956007 -0.849537
..        ...       ...
47   1.316541 -1.005670
526  0.337959 -0.692820
67  -1.660696 -2.607392
637  1.082437 -1.555458
96   1.149389 -1.030593

[1000 rows x 9 columns], 'y': 829    -9.531238
201   -10.714346
561     8.271140
370     6.533270
921     0.366548
         ...
47     11.849394
526    20.291162
67     -0.972001
637   -10.850731
96     14.140134
Name: y, Length: 1000, dtype: float64, 'treatment': 829    False
201    False
561     True
370     True
921    False
       ...
47      True
526     True
67      True
637    False
96      True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    Gini   (Control):     0.7658
INFO:causalml:    Gini (Treatment):     0.9872
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0254
INFO:causalml:    RMSE (Treatment):     0.9947
INFO:causalml:   sMAPE   (Control):     0.5244
INFO:causalml:   sMAPE (Treatment):     0.1842
INFO:causalml:    Gini   (Control):     0.6910
INFO:causalml:    Gini (Treatment):     0.9870

{'X':            W4        W2        W1        W3        W0        X1        X0  \
461 -1.460257 -2.060113  0.196816  0.509257 -0.190118  0.640799  1.125688
27  -0.764321  0.661496 -1.554778 -0.379143 -0.596560 -1.612376  0.853551
546 -2.764439  0.366811  0.121471  0.269416 -1.306595 -0.411430  1.901790
222 -0.935723  1.269480  0.057024 -2.368038 -0.694916 -0.264277  1.826955
936 -0.594141 -0.764907 -0.392504 -0.899154 -1.017892 -2.733821  1.305824
..        ...       ...       ...       ...       ...       ...       ...
353  0.015998  0.982657 -0.392354 -0.194599 -1.400468  0.281264  2.355602
366 -1.330955  1.319725  1.896027 -0.476799  1.671268  1.552792  0.045233
930  0.223896  0.462045 -0.598557 -0.386784  0.146834  1.176468 -0.409971
20  -0.912271  1.216225 -0.653480  1.130985 -0.866986  0.547937  1.677859
517 -1.362621 -0.010860 -0.360578  1.211214 -0.976836 -1.004424  1.073958

           X0        X1
461  1.125688  0.640799
27   0.853551 -1.612376
546  1.901790 -0.411430
222  1.826955 -0.264277
936  1.305824 -2.733821
..        ...       ...
353  2.355602  0.281264
366  0.045233  1.552792
930 -0.409971  1.176468
20   1.677859  0.547937
517  1.073958 -1.004424

[1000 rows x 9 columns], 'y': 461    -6.434741
27     -3.423349
546   -11.053337
222     9.575547
936     4.998172
         ...
353    16.031327
366    13.879918
930    10.114187
20     14.509975
517    -4.981074
Name: y, Length: 1000, dtype: float64, 'treatment': 461    False
27     False
546    False
222     True
936     True
       ...
353     True
366     True
930     True
20      True
517    False
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1047
INFO:causalml:    RMSE (Treatment):     1.0173
INFO:causalml:   sMAPE   (Control):     0.5298
INFO:causalml:   sMAPE (Treatment):     0.1928
INFO:causalml:    Gini   (Control):     0.7360
INFO:causalml:    Gini (Treatment):     0.9868

{'X':            W4        W2        W1        W3        W0        X1        X0  \
879 -1.256963  2.610828  0.183906 -0.127617 -1.530665  0.406917 -0.949262
507 -1.625075 -0.163592 -1.552466  0.974451 -1.413166 -2.116304  0.715120
5    0.117539 -0.237488 -0.331980 -1.762631  0.041748 -0.904643 -0.577618
503 -1.927225  2.080508 -1.491481  0.302042  0.310731 -0.307241  1.247553
398 -0.421897  1.548984 -1.098997  0.506289 -1.730921 -1.446963  0.408468
..        ...       ...       ...       ...       ...       ...       ...
27  -0.754819  0.708271 -1.279052 -0.231437 -0.561031 -1.538710  1.170755
772 -1.641683  0.455094 -1.104356  1.465097 -0.395100 -1.410726 -0.320576
509  0.454215  0.351701 -0.295182  0.429296  0.286542 -1.281868 -0.422850
643 -0.714246  2.659219 -1.015225  1.523564 -3.134931  1.156423  1.157304
998 -0.208078  0.901069 -1.635665 -0.081512 -1.318741  0.756215  1.485413

           X0        X1
879 -0.949262  0.406917
507  0.715120 -2.116304
5   -0.577618 -0.904643
503  1.247553 -0.307241
398  0.408468 -1.446963
..        ...       ...
27   1.170755 -1.538710
772 -0.320576 -1.410726
509 -0.422850 -1.281868
643  1.157304  1.156423
998  1.485413  0.756215

[1000 rows x 9 columns], 'y': 879     2.556286
507    -8.258449
5       4.741680
503    -3.163402
398    -5.105764
         ...
27     -3.423349
772     3.250030
509    10.361696
643     9.118838
998    13.321122
Name: y, Length: 1000, dtype: float64, 'treatment': 879     True
507    False
5       True
503    False
398    False
       ...
27     False
772     True
509     True
643     True
998     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0923
INFO:causalml:    RMSE (Treatment):     0.8748
INFO:causalml:   sMAPE   (Control):     0.5358
INFO:causalml:   sMAPE (Treatment):     0.1645
INFO:causalml:    Gini   (Control):     0.6935
INFO:causalml:    Gini (Treatment):     0.9895

{'X':            W4        W2        W1        W3        W0        X1        X0  \
182 -0.858135  0.548027  0.570832  1.543022 -2.357673  1.666628  2.422103
581 -0.420050  2.640835 -1.237284 -0.816649 -1.431552 -1.135704  1.275244
381  0.220628  0.129095 -0.082455 -0.835143 -1.065214 -0.080231 -1.145132
877 -0.263394  0.773930 -0.167360 -1.809891  1.394062  1.191594  0.087068
123 -0.820337  1.260770 -0.463931  1.269620 -2.509042  1.125975 -1.084071
..        ...       ...       ...       ...       ...       ...       ...
413 -0.352489  1.333771 -2.830226 -0.970291 -2.868858 -0.818934  0.160747
725 -1.069626  1.185964 -2.415549  0.944403 -0.280930  1.234655 -1.314084
300 -1.442654  0.561198 -0.598452  1.170720 -1.425032 -0.104172  2.707997
533 -1.916023  2.135901 -0.882891 -0.073193 -2.437310 -1.498964 -0.956094
746  0.635679  0.256677 -0.002004 -0.305042  0.331563  0.584977 -0.708696

           X0        X1
182  2.422103  1.666628
581  1.275244 -1.135704
381 -1.145132 -0.080231
877  0.087068  1.191594
123 -1.084071  1.125975
..        ...       ...
413  0.160747 -0.818934
725 -1.314084  1.234655
300  2.707997 -0.104172
533 -0.956094 -1.498964
746 -0.708696  0.584977

[1000 rows x 9 columns], 'y': 182    15.277732
581    10.512753
381     2.059536
877    12.698322
123     1.168663
         ...
413   -10.466301
725    -3.118501
300    13.493938
533    -6.739186
746    10.903542
Name: y, Length: 1000, dtype: float64, 'treatment': 182     True
581     True
381     True
877     True
123     True
       ...
413    False
725    False
300     True
533     True
746     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0416
INFO:causalml:    RMSE (Treatment):     1.0407
INFO:causalml:   sMAPE   (Control):     0.5279
INFO:causalml:   sMAPE (Treatment):     0.1668
INFO:causalml:    Gini   (Control):     0.6798
INFO:causalml:    Gini (Treatment):     0.9840
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9505
INFO:causalml:    RMSE (Treatment):     0.9570
INFO:causalml:   sMAPE   (Control):     0.5073
INFO:causalml:   sMAPE (Treatment):     0.1837
INFO:causalml:    Gini   (Control):     0.7110
INFO:causalml:    Gini (Treatment):     0.9879

{'X':            W4        W2        W1        W3        W0        X1        X0  \
521 -1.064810  2.125579 -2.096933  0.238321 -1.922263 -0.778079 -0.661939
277 -0.958657  0.357395  0.086754 -0.898277 -0.734021 -1.876077 -0.094653
161 -2.661257  1.594218 -1.232403 -1.302777 -1.473713  1.516190  2.922502
708 -0.420950  1.218293  0.079718 -0.321749 -0.314454 -1.630925  1.536317
810 -0.607251  0.596658  0.352069  0.448846 -0.316254 -0.086533 -0.948286
..        ...       ...       ...       ...       ...       ...       ...
549  1.102751  0.590279 -1.566217 -0.906844  0.194944 -0.075375  0.976922
735 -0.530474  0.449591 -2.149441 -0.581266  0.593925 -1.157048  2.266023
850 -1.613409  1.467119  0.187807  1.032850 -2.193383 -0.478503  2.370121
800 -2.437974  0.813644  0.292719  1.129961  0.010590  0.636060 -1.370904
282 -0.160975 -0.195619  0.574784  1.624199 -1.756967 -1.462474  0.626548

           X0        X1
521 -0.661939 -0.778079
277 -0.094653 -1.876077
161  2.922502  1.516190
708  1.536317 -1.630925
810 -0.948286 -0.086533
..        ...       ...
549  0.976922 -0.075375
735  2.266023 -1.157048
850  2.370121 -0.478503
800 -1.370904  0.636060
282  0.626548 -1.462474

[1000 rows x 9 columns], 'y': 521     0.541061
277     1.863805
161   -11.664097
708    12.369009
810     5.956647
         ...
549    15.679880
735    15.212283
850    12.795708
800     2.492589
282     7.672536
Name: y, Length: 1000, dtype: float64, 'treatment': 521     True
277     True
161    False
708     True
810     True
       ...
549     True
735     True
850     True
800     True
282     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
335 -1.100336  1.977221  0.607966 -0.793885 -2.545464  1.366718  0.809280
171 -0.991677  1.353156 -1.437487 -0.719049 -1.376758  0.623510 -0.261505
148 -0.250226 -0.427685 -1.350273 -0.859032 -1.316345 -1.337075  1.060074
337 -2.354606  0.835613 -1.931366 -0.876002 -0.937969 -0.123990 -0.279434
550 -0.407100 -0.483698  0.309698 -0.047278  0.531595 -1.814945  0.574206
..        ...       ...       ...       ...       ...       ...       ...
899 -0.628691 -0.756683 -0.238593 -1.000216 -1.325123 -0.779234 -0.059174
796 -0.102175  1.460876 -0.167504 -1.664169 -0.077747  0.248961 -0.648353
898 -1.258328  1.939644 -0.848240  1.668798  0.619521 -1.431741  2.092603
399 -1.386327  2.762234  0.245965  0.029650  1.174612 -2.486153  0.375883
982 -0.803224  0.691163 -0.389384 -0.943258  0.847790 -1.253997  1.159258

           X0        X1
335  0.809280  1.366718
171 -0.261505  0.623510
148  1.060074 -1.337075
337 -0.279434 -0.123990
550  0.574206 -1.814945
..        ...       ...
899 -0.059174 -0.779234
796 -0.648353  0.248961
898  2.092603 -1.431741
399  0.375883 -2.486153
982  1.159258 -1.253997

[1000 rows x 9 columns], 'y': 335     6.324474
171     2.573635
148     4.946877
337   -10.149884
550     9.910321
         ...
899     2.709571
796    -1.447993
898    17.331546
399    10.567009
982    12.003112
Name: y, Length: 1000, dtype: float64, 'treatment': 335     True
171     True
148     True
337    False
550     True
       ...
899     True
796    False
898     True
399     True
982     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0987
INFO:causalml:    RMSE (Treatment):     1.0058
INFO:causalml:   sMAPE   (Control):     0.5550
INFO:causalml:   sMAPE (Treatment):     0.1759
INFO:causalml:    Gini   (Control):     0.7364
INFO:causalml:    Gini (Treatment):     0.9863

{'X':            W4        W2        W1        W3        W0        X1        X0  \
256 -0.665575  1.778065 -0.968217 -0.127747  0.334922 -0.370859  2.847488
417 -0.457640  0.285454 -1.058043 -0.216185 -0.169154 -0.836608  1.797101
919  0.785385  1.126725 -0.775216 -0.702748  0.504256 -2.635844  0.545678
996 -3.019533  1.626102 -0.388613  1.772885  1.258386  0.954511  1.826523
821 -1.469025  1.208626 -3.567248 -0.517454 -1.519949  0.844796 -0.477846
..        ...       ...       ...       ...       ...       ...       ...
667 -1.941159  0.823270 -0.024040  1.252969 -0.146037  0.363587  1.403426
176 -0.863464  1.495569  0.864471  0.072182 -2.735479 -0.175786 -0.386896
761 -1.710151 -0.234718 -0.890572 -1.456842  1.121008 -0.747674  0.901646
114 -0.344432  0.510373 -1.191251  0.334169 -1.996054 -1.308779 -0.613666
132 -4.244958  1.454520 -0.853378  1.165462 -2.200121  0.261365  0.329199

           X0        X1
256  2.847488 -0.370859
417  1.797101 -0.836608
919  0.545678 -2.635844
996  1.826523  0.954511
821 -0.477846  0.844796
..        ...       ...
667  1.403426  0.363587
176 -0.386896 -0.175786
761  0.901646 -0.747674
114 -0.613666 -1.308779
132  0.329199  0.261365

[1000 rows x 9 columns], 'y': 256    20.538593
417    12.283023
919    12.794015
996    17.211685
821    -9.602174
         ...
667    11.985876
176     0.810587
761     7.959767
114     0.119504
132    -3.629107
Name: y, Length: 1000, dtype: float64, 'treatment': 256     True
417     True
919     True
996     True
821    False
       ...
667     True
176     True
761     True
114     True
132     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9008
INFO:causalml:    RMSE (Treatment):     0.9300
INFO:causalml:   sMAPE   (Control):     0.5453
INFO:causalml:   sMAPE (Treatment):     0.1736
INFO:causalml:    Gini   (Control):     0.7715
INFO:causalml:    Gini (Treatment):     0.9875
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
435 -1.290773  1.787665 -0.154730  0.309899 -0.615941  1.055156  1.949821
571  0.073259 -0.265020  1.336202  0.316344  0.232892 -0.748365  1.455606
925 -0.241368 -0.555981 -1.886168  1.751812 -1.451546 -1.286078  0.058359
191 -0.167786 -0.382653 -2.050806  0.247710 -1.377576  0.097655 -0.163738
857 -0.880414  0.756512 -0.296519 -1.008379 -0.378510 -0.025587 -0.555009
..        ...       ...       ...       ...       ...       ...       ...
304 -0.657374  0.819624  1.589740  2.162585  0.989363  0.796753  1.805996
732 -1.854802  1.650649 -0.613165  0.707549 -0.035249 -0.823780  2.115949
469 -1.857866  0.189421  0.310381 -1.897358 -1.033174 -1.519147  1.891197
139 -1.429328  0.994971  0.445033  0.053998  0.689442 -1.102233  0.834170
405 -3.090734 -0.161113 -0.615230 -0.393858  0.470887 -1.691078 -0.777247

           X0        X1
435  1.949821  1.055156
571  1.455606 -0.748365
925  0.058359 -1.286078
191 -0.163738  0.097655
857 -0.555009 -0.025587
..        ...       ...
304  1.805996  0.796753
732  2.115949 -0.823780
469  1.891197 -1.519147
139  0.834170 -1.102233
405 -0.777247 -1.691078

[1000 rows x 9 columns], 'y': 435    13.857254
571    16.753939
925     4.053751
191    -5.492128
857     4.361211
         ...
304    22.259686
732    -3.133831
469     4.435916
139    11.778033
405    -8.503448
Name: y, Length: 1000, dtype: float64, 'treatment': 435     True
571     True
925     True
191    False
857     True
       ...
304     True
732    False
469     True
139     True
405    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
373 -0.586068  0.785214 -0.258682  2.335104 -0.514019  0.671037 -0.047911
740 -1.730376 -0.082488 -0.860927  1.462483  0.614116 -0.480573  2.059247
501 -1.551334  1.009595  1.256427 -0.197566 -2.084231 -0.446778  0.288796
220  0.878591  1.244577 -0.156619  0.477556 -1.101665 -1.706181  0.564847
176 -0.686087  1.487416  0.889637  0.124132 -2.654856 -0.027463 -0.529537
..        ...       ...       ...       ...       ...       ...       ...
975 -1.038536  0.544160  0.204063  2.023977 -0.641787 -1.154168  0.788529
783 -1.590418  1.712067 -1.071416 -2.106101 -0.628625  0.294950  0.044412
845 -1.343336 -0.556841 -1.157557  0.820337 -1.689095  0.090289 -0.518620
731 -0.748152  2.179917 -1.198134  0.162754 -0.255301 -0.187085  1.350099
437 -1.814920  2.655633  0.842024  0.082480 -1.483412 -1.248605 -0.208832

           X0        X1
373 -0.047911  0.671037
740  2.059247 -0.480573
501  0.288796 -0.446778
220  0.564847 -1.706181
176 -0.529537 -0.027463
..        ...       ...
975  0.788529 -1.154168
783  0.044412  0.294950
845 -0.518620  0.090289
731  1.350099 -0.187085
437 -0.208832 -1.248605

[1000 rows x 9 columns], 'y': 373    12.268099
740    15.715393
501     2.593155
220    10.878908
176     0.810587
         ...
975     9.860888
783     2.764437
845    -8.309502
731    13.912472
437     1.751543
Name: y, Length: 1000, dtype: float64, 'treatment': 373     True
740     True
501     True
220     True
176     True
       ...
975     True
783     True
845    False
731     True
437     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.2452
INFO:causalml:    RMSE (Treatment):     1.0504
INFO:causalml:   sMAPE   (Control):     0.5990
INFO:causalml:   sMAPE (Treatment):     0.1656
INFO:causalml:    Gini   (Control):     0.7487
INFO:causalml:    Gini (Treatment):     0.9861
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1500
INFO:causalml:    RMSE (Treatment):     1.0641
INFO:causalml:   sMAPE   (Control):     0.5799
INFO:causalml:   sMAPE (Treatment):     0.1953
INFO:causalml:    Gini   (Control):     0.7139
INFO:causalml:    Gini (Treatment):     0.9853
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
476 -0.916246  0.716053  1.561100  1.401698 -0.298811  1.040683  1.057571
96   1.086606  0.844974 -0.116830  0.902299 -1.404204 -1.070461  1.049181
350 -0.488393  1.011535  0.916039  0.266851 -0.692067 -1.712296  0.453096
247 -1.185833  0.211358  0.441802  0.973059 -0.087143 -1.599477 -0.505370
85  -1.717485  1.374249 -0.641572 -0.273738 -0.978240 -2.893886 -0.306468
..        ...       ...       ...       ...       ...       ...       ...
676 -1.635484  2.555525 -0.922551  1.692212 -2.022160  0.377001  0.911202
955 -0.502191  0.691452 -1.555526  1.187045  1.790498 -0.879598  1.108326
624 -2.064699 -0.396411  0.015957  1.326630 -1.316539  0.201398  1.078927
164 -2.005248  0.401852 -1.029118  0.011918 -1.026978 -1.102522  0.124741
614 -2.496101  1.064544 -0.721495  0.237056  0.136538 -0.549725 -0.119093

           X0        X1
476  1.057571  1.040683
96   1.049181 -1.070461
350  0.453096 -1.712296
247 -0.505370 -1.599477
85  -0.306468 -2.893886
..        ...       ...
676  0.911202  0.377001
955  1.108326 -0.879598
624  1.078927  0.201398
164  0.124741 -1.102522
614 -0.119093 -0.549725

[1000 rows x 9 columns], 'y': 476    13.134949
96     14.140134
350     8.901755
247     3.320266
85     -0.384362
         ...
676     8.554497
955    18.503792
624     5.810979
164     1.757613
614    -5.992837
Name: y, Length: 1000, dtype: float64, 'treatment': 476     True
96      True
350     True
247     True
85      True
       ...
676     True
955     True
624     True
164     True
614    False
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0893
INFO:causalml:    RMSE (Treatment):     1.0560
INFO:causalml:   sMAPE   (Control):     0.5163
INFO:causalml:   sMAPE (Treatment):     0.1890
INFO:causalml:    Gini   (Control):     0.6924
INFO:causalml:    Gini (Treatment):     0.9841
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1185
INFO:causalml:    RMSE (Treatment):     1.0528
INFO:causalml:   sMAPE   (Control):     0.4944
INFO:causalml:   sMAPE (Treatment):     0.1829
INFO:causalml:    Gini   (Control):     0.6812
INFO:causalml:    Gini (Treatment):     0.9845
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0523
INFO:causalml:    RMSE (Treatment):     1.0966
INFO:causalml:   sMAPE   (Control):     0.5335
INFO:causalml:   sMAPE (Treatment):     0.1846
INFO:causalml:    Gini   (Control):     0.7403
INFO:causalml:    Gini (Treatment):     0.9842

{'X':            W4        W2        W1        W3        W0        X1        X0  \
656 -1.365084  1.819157  2.098686  0.874874  1.299507 -0.600109  0.545343
607 -0.706812 -0.877614 -0.315875 -0.408886 -0.483012 -0.921114 -0.109378
342 -1.679826 -0.614213 -1.995008 -0.124394  0.000163 -1.293688  0.397625
843 -1.884440  2.477489  1.248540  1.445514 -0.693600 -1.420867 -0.105174
574 -0.478880  0.360457 -1.256787  1.173270 -0.719220 -1.092105 -0.236187
..        ...       ...       ...       ...       ...       ...       ...
348 -0.907488  1.956598 -1.850559  0.326344 -1.404297 -1.670213  1.590651
181  1.288764  0.127013 -0.064689  0.743168 -0.541843 -0.899438  0.210216
126 -0.216085  2.137236  0.513100 -0.728803 -0.439462 -1.680363  0.199879
804 -2.459387  0.782972 -0.055967  1.354856 -1.178010 -1.081715  0.546584
741 -0.609507  2.797551 -1.293786 -0.905862 -1.302123 -0.453713  0.131056

           X0        X1
656  0.545343 -0.600109
607 -0.109378 -0.921114
342  0.397625 -1.293688
843 -0.105174 -1.420867
574 -0.236187 -1.092105
..        ...       ...
348  1.590651 -1.670213
181  0.210216 -0.899438
126  0.199879 -1.680363
804  0.546584 -1.081715
741  0.131056 -0.453713

[1000 rows x 9 columns], 'y': 656    15.632059
607     3.571733
342    -6.856146
843     6.032900
574     5.633909
         ...
348     9.469476
181    13.569762
126     9.175909
804     2.906483
741     5.593102
Name: y, Length: 1000, dtype: float64, 'treatment': 656     True
607     True
342    False
843     True
574     True
       ...
348     True
181     True
126     True
804     True
741     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
766 -0.756667  0.367923  1.153857  1.799840  0.228541 -0.160941  0.239082
295 -0.920244  0.816180 -1.724668  0.524165 -1.332265  0.253598  1.161850
413 -0.358793  1.190467 -2.753001 -1.067620 -3.125181 -0.715718 -0.055546
293 -1.984653 -1.143055 -1.910437 -1.334067 -0.775120 -1.530153  0.337544
669 -0.053879  0.915218 -1.183049  0.444186  0.486040 -2.352234 -0.881775
..        ...       ...       ...       ...       ...       ...       ...
475 -1.420390 -1.741700 -1.396914 -1.520924 -0.835104 -0.095234  2.793876
136 -1.755542 -0.374752 -1.002181  1.103858  0.006548  0.715375 -0.163707
421 -2.105743  0.172084 -0.756788 -0.443594 -0.113048  0.950566 -2.126405
857 -0.822371  0.714026 -0.458390 -0.895779 -0.553540  0.003880 -0.725634
977 -1.684132  0.534968 -1.003727  0.342221  0.432128 -0.191376  1.501638

           X0        X1
766  0.239082 -0.160941
295  1.161850  0.253598
413 -0.055546 -0.715718
293  0.337544 -1.530153
669 -0.881775 -2.352234
..        ...       ...
475  2.793876 -0.095234
136 -0.163707  0.715375
421 -2.126405  0.950566
857 -0.725634  0.003880
977  1.501638 -0.191376

[1000 rows x 9 columns], 'y': 766    12.119461
295     9.933260
413   -10.466301
293   -11.609205
669     6.961904
         ...
475   -10.586297
136    -4.977571
421    -2.608507
857     4.361211
977    -3.568162
Name: y, Length: 1000, dtype: float64, 'treatment': 766     True
295     True
413    False
293    False
669     True
       ...
475    False
136    False
421     True
857     True
977    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
716 -1.780908  1.296653 -1.506171 -1.069888  1.365460 -0.138733  0.313991
932 -1.299847  0.588967 -0.107455  0.283732 -1.323747  0.130964 -0.733880
582 -1.949519  1.287445 -0.375304  1.762713 -0.174648 -0.675732  0.814971
28   0.228184 -1.669546 -0.676058 -1.615774 -0.315091 -0.476678 -0.171973
761 -1.902200 -0.095299 -0.949139 -1.353268  0.986338 -0.829036  0.806286
..        ...       ...       ...       ...       ...       ...       ...
207 -1.325801  1.102182  1.192057  0.460207  1.410201 -2.357977  0.753458
914 -0.005436  1.224618  0.435307 -0.579745 -0.861379  0.677591  0.691308
321 -2.029734 -0.232718  0.728393  0.233231 -0.613002 -0.260258  0.602071
774 -0.740094  0.123906  0.391809 -0.824587  0.195687 -1.391723  0.153809
41   0.107240 -0.096356 -0.831740  2.298990 -0.212071 -0.017084  0.168104

           X0        X1
716  0.313991 -0.138733
932 -0.733880  0.130964
582  0.814971 -0.675732
28  -0.171973 -0.476678
761  0.806286 -0.829036
..        ...       ...
207  0.753458 -2.357977
914  0.691308  0.677591
321  0.602071 -0.260258
774  0.153809 -1.391723
41   0.168104 -0.017084

[1000 rows x 9 columns], 'y': 716     9.358799
932     1.654133
582    10.549091
28      4.753158
761     7.959767
         ...
207    13.068250
914    12.801840
321    -6.946700
774     6.080336
41     12.011428
Name: y, Length: 1000, dtype: float64, 'treatment': 716     True
932     True
582     True
28      True
761     True
       ...
207     True
914     True
321    False
774     True
41      True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.2193
INFO:causalml:    RMSE (Treatment):     0.9058
INFO:causalml:   sMAPE   (Control):     0.5661
INFO:causalml:   sMAPE (Treatment):     0.1605
INFO:causalml:    Gini   (Control):     0.6898
INFO:causalml:    Gini (Treatment):     0.9892
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0219
INFO:causalml:    RMSE (Treatment):     0.8973
INFO:causalml:   sMAPE   (Control):     0.5490
INFO:causalml:   sMAPE (Treatment):     0.1593

{'X':            W4        W2        W1        W3        W0        X1        X0  \
535 -0.408946 -0.549911 -0.438350  0.654294 -0.431326 -1.569354  2.919030
813 -2.613072  1.682590 -1.177321  0.400974  0.448995 -0.975483  0.266881
702  1.737639  1.568111 -0.021695 -0.401659  1.804794  0.047483  0.487667
520 -1.447681  0.708456 -0.474129 -0.049508 -0.575110 -0.862941  1.229650
489 -0.389307 -1.466011 -0.718566  0.734077  0.085211 -1.077799 -1.288199
..        ...       ...       ...       ...       ...       ...       ...
961 -1.446214  1.654762 -0.271929  1.143991 -0.885921 -1.901416  0.019326
57   0.172187  0.239910 -0.921379  0.628416  0.606811 -0.931338  2.313877
541  0.332641 -0.475919 -0.267263 -1.407782  0.013615 -0.561815 -0.244886
659 -0.818998  2.868143 -0.850733  2.637302 -1.828988 -0.761172 -0.675929
877 -0.220138  0.794346 -0.213728 -1.750195  1.452935  1.435132 -0.171223

           X0        X1
535  2.919030 -1.569354
813  0.266881 -0.975483
702  0.487667  0.047483
520  1.229650 -0.862941
489 -1.288199 -1.077799
..        ...       ...
961  0.019326 -1.901416
57   2.313877 -0.931338
541 -0.244886 -0.561815
659 -0.675929 -0.761172
877 -0.171223  1.435132

[1000 rows x 9 columns], 'y': 535    16.319324
813     5.390940
702    23.877017
520    -5.145441
489     2.992182
         ...
961     4.732956
57     18.788397
541     5.970889
659     4.772285
877    12.698322
Name: y, Length: 1000, dtype: float64, 'treatment': 535     True
813     True
702     True
520    False
489     True
       ...
961     True
57      True
541     True
659     True
877     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
809 -1.752726  1.478834  0.359537  1.257853 -1.355993 -0.610185  0.942202
474 -0.345282  2.676627 -0.363979  0.739986  1.029841 -0.808572  0.177291
51  -0.311615  0.463675  1.315166 -0.177182 -0.411103 -0.774316 -0.120261
706 -0.692079  2.126244  0.262442  0.057324  0.769064 -0.869747  2.342587
42   0.652502  0.343937 -0.589744  1.123646  0.894766  0.882823  0.656313
..        ...       ...       ...       ...       ...       ...       ...
519 -1.076340  1.531144 -0.136154 -0.171633 -1.209391 -0.787394  0.436260
982 -0.838780  0.578530 -0.508228 -0.862035  0.874482 -1.286068  1.319914
736 -0.812325  1.159239 -1.139383  1.176134 -1.104442 -1.210979  0.111341
806 -0.847931 -0.003755  0.040395  0.548220 -0.999150 -2.048184  1.979963
811 -1.703399  0.515531 -1.691708  1.993570 -0.182223 -2.128436  1.939605

           X0        X1
809  0.942202 -0.610185
474  0.177291 -0.808572
51  -0.120261 -0.774316
706  2.342587 -0.869747
42   0.656313  0.882823
..        ...       ...
519  0.436260 -0.787394
982  1.319914 -1.286068
736  0.111341 -1.210979
806  1.979963 -2.048184
811  1.939605 -2.128436

[1000 rows x 9 columns], 'y': 809     7.084273
474    14.285230
51      8.374283
706    19.485830
42     19.574715
         ...
519     6.513308
982    12.003112
736     5.459094
806    11.154196
811    10.541175
Name: y, Length: 1000, dtype: float64, 'treatment': 809    True
474    True
51     True
706    True
42     True
       ...
519    True
982    True
736    True
806    True
811    True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    Gini   (Control):     0.7354
INFO:causalml:    Gini (Treatment):     0.9894
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9566
INFO:causalml:    RMSE (Treatment):     0.9046
INFO:causalml:   sMAPE   (Control):     0.5404
INFO:causalml:   sMAPE (Treatment):     0.1517
INFO:causalml:    Gini   (Control):     0.7800
INFO:causalml:    Gini (Treatment):     0.9892

{'X':            W4        W2        W1        W3        W0        X1        X0  \
710 -0.885544  3.109400 -1.161211 -0.183391 -0.929634 -0.717760 -0.422444
288 -1.778829  1.792842  0.514340  0.704523 -1.436816  0.250296  0.553171
21  -0.748560  0.992715 -0.587886  1.191375  0.068951 -1.902454 -0.219831
852 -3.079856  0.894105 -1.545568  0.883254  0.925119 -1.911790  0.642795
605 -0.743595  1.049269 -1.274067  1.230038 -0.519646 -1.730869  0.754537
..        ...       ...       ...       ...       ...       ...       ...
61  -0.611891  0.230504 -0.106735  0.888671 -1.792676 -2.140587 -1.199169
236 -0.823569  1.054378  0.015185 -1.300129 -1.944497  0.868855  0.267289
385 -0.047892 -0.133264 -0.328396  0.389706 -1.938165 -1.366609 -1.458593
102 -0.272519 -0.441834 -1.705100  1.615818  0.227272 -0.903417  1.048783
818 -2.314737  0.952842 -0.240204  0.218917 -2.342249 -0.645204  1.335878

           X0        X1
710 -0.422444 -0.717760
288  0.553171  0.250296
21  -0.219831 -1.902454
852  0.642795 -1.911790
605  0.754537 -1.730869
..        ...       ...
61  -1.199169 -2.140587
236  0.267289  0.868855
385 -1.458593 -1.366609
102  1.048783 -0.903417
818  1.335878 -0.645204

[1000 rows x 9 columns], 'y': 710     5.904376
288     5.235864
21      6.406231
852     4.981610
605    -1.888067
         ...
61     -3.292629
236     4.765758
385    -1.265529
102    12.024923
818   -11.770683
Name: y, Length: 1000, dtype: float64, 'treatment': 710     True
288     True
21      True
852     True
605    False
       ...
61      True
236     True
385     True
102     True
818    False
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0569
INFO:causalml:    RMSE (Treatment):     0.9878
INFO:causalml:   sMAPE   (Control):     0.5790
INFO:causalml:   sMAPE (Treatment):     0.1811
INFO:causalml:    Gini   (Control):     0.7231
INFO:causalml:    Gini (Treatment):     0.9886
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0845

{'X':            W4        W2        W1        W3        W0        X1        X0  \
358 -1.618982  0.336568 -0.776039  1.448581 -1.102834 -0.364277  0.374769
540 -1.561058  2.046827 -0.447731 -0.203809  2.188471  0.651892  1.592926
74  -1.264685 -0.089928 -2.538475 -2.032437  1.258205 -2.129692  1.397599
189  0.623183  0.339064  1.379526 -0.573508 -3.246150 -0.459409  1.117661
122 -3.033143 -0.118046 -1.231457  0.914404  0.023980 -1.772121  0.337744
..        ...       ...       ...       ...       ...       ...       ...
223 -0.861923  0.334306  0.352111  1.762110 -1.820752 -1.139081  0.854460
254 -1.269406 -0.739805 -1.201137  0.641957  0.176942 -0.232125 -1.387830
493 -0.951445  2.289435 -1.354908  0.358001 -0.221044 -0.014761 -0.799413
883 -1.192269  0.013901 -0.092697 -0.181750 -1.393782  1.118679  0.530328
26   0.006493 -0.018154  0.263089  2.895273 -0.498851 -2.131243  2.267322

           X0        X1
358  0.374769 -0.364277
540  1.592926  0.651892
74   1.397599 -2.129692
189  1.117661 -0.459409
122  0.337744 -1.772121
..        ...       ...
223  0.854460 -1.139081
254 -1.387830 -0.232125
493 -0.799413 -0.014761
883  0.530328  1.118679
26   2.267322 -2.131243

[1000 rows x 9 columns], 'y': 358     3.577322
540    19.517995
74      8.688916
189     5.702358
122    -8.281570
         ...
223     6.636087
254     0.689958
493     4.987087
883     5.901248
26     17.468518
Name: y, Length: 1000, dtype: float64, 'treatment': 358     True
540     True
74      True
189     True
122    False
       ...
223     True
254     True
493     True
883     True
26      True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
240 -1.263348  1.162763  0.105228  1.205755 -0.119909 -1.028827 -0.167676
498  0.810539  1.373755  0.219523  0.539263  0.138168 -0.144650  0.582504
529  0.630397 -0.221831 -0.178235 -0.483179 -2.770740 -1.892844  0.415426
457 -1.354105  1.457178 -0.777315  1.883983 -2.976922 -1.550290 -0.125306
2   -1.608387 -0.291487 -0.839419 -0.258104 -1.204207 -0.514901 -0.399287
..        ...       ...       ...       ...       ...       ...       ...
929 -1.676347  1.563545 -0.515152  0.216150 -1.656749  0.514116 -0.605672
197 -1.603567  0.713467 -2.349028  2.255348 -1.048802 -0.635757  1.187067
750 -0.265670  2.101574 -2.127944 -0.954356  0.096180  0.364974  0.481835
349 -1.826042  1.028645 -0.118728  0.401023 -0.059445 -0.562672  1.065773
477 -3.013714  0.861584 -0.071117  1.361774 -1.356168 -0.940111  1.860255

           X0        X1
240 -0.167676 -1.028827
498  0.582504 -0.144650
529  0.415426 -1.892844
457 -0.125306 -1.550290
2   -0.399287 -0.514901
..        ...       ...
929 -0.605672  0.514116
197  1.187067 -0.635757
750  0.481835  0.364974
349  1.065773 -0.562672
477  1.860255 -0.940111

[1000 rows x 9 columns], 'y': 240     6.512254
498    17.581110
529     3.288937
457    -8.028104
2      -9.777346
         ...
929     0.739416
197    -4.810420
750    -1.151925
349    10.315484
477     7.037455
Name: y, Length: 1000, dtype: float64, 'treatment': 240     True
498     True
529     True
457    False
2      False
       ...
929     True
197    False
750    False
349     True
477     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    RMSE (Treatment):     0.9745
INFO:causalml:   sMAPE   (Control):     0.5396
INFO:causalml:   sMAPE (Treatment):     0.1887
INFO:causalml:    Gini   (Control):     0.7519
INFO:causalml:    Gini (Treatment):     0.9890
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0117
INFO:causalml:    RMSE (Treatment):     0.9768
INFO:causalml:   sMAPE   (Control):     0.4914
INFO:causalml:   sMAPE (Treatment):     0.1746
INFO:causalml:    Gini   (Control):     0.7261
INFO:causalml:    Gini (Treatment):     0.9861
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8418
INFO:causalml:    RMSE (Treatment):     1.0209
INFO:causalml:   sMAPE   (Control):     0.4726
INFO:causalml:   sMAPE (Treatment):     0.1868

{'X':            W4        W2        W1        W3        W0        X1        X0  \
273 -0.677619  1.186574 -0.958007  1.376116 -1.854810 -0.737435 -2.090940
597 -0.418731  0.117396 -1.426846  2.216330 -0.924005  0.298973 -0.106910
840  0.815459  0.748352  0.500100  1.252534 -0.255523 -0.079447  0.564455
24  -0.617694  0.450154 -1.288916  1.397034 -2.702385 -0.985369  0.468185
520 -1.383487  0.444018 -0.638238  0.000878 -0.541441 -0.933725  1.018526
..        ...       ...       ...       ...       ...       ...       ...
111  1.145629  0.629617 -0.273566  1.928849 -0.574426 -0.809487  0.827795
990  0.189329  0.975162 -0.079997  0.203722 -1.239292  0.415439 -0.101237
669  0.261455  1.204097 -0.832557  0.498671  0.600173 -2.208832 -0.891973
935 -1.605048  0.441036  0.273670  1.925556 -1.113346  0.059029  0.672250
569 -2.807598  0.429742  0.681303  2.110926  1.443053 -1.329212  2.269497

           X0        X1
273 -2.090940 -0.737435
597 -0.106910  0.298973
840  0.564455 -0.079447
24   0.468185 -0.985369
520  1.018526 -0.933725
..        ...       ...
111  0.827795 -0.809487
990 -0.101237  0.415439
669 -0.891973 -2.208832
935  0.672250  0.059029
569  2.269497 -1.329212

[1000 rows x 9 columns], 'y': 273    -2.115646
597    -1.143963
840    15.748095
24     -7.244556
520    -5.145441
         ...
111    16.735009
990     8.714440
669     6.961904
935     7.518694
569    16.043709
Name: y, Length: 1000, dtype: float64, 'treatment': 273     True
597    False
840     True
24     False
520    False
       ...
111     True
990     True
669     True
935     True
569     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
712 -0.414558  0.791334 -0.285844  1.287638 -0.036979 -0.636977 -0.815817
391 -0.372872  1.188105  1.332948 -1.096777  0.691069  0.171162  0.169036
20  -1.011585  1.257003 -0.535764  1.111335 -0.895269  0.514914  1.672974
470 -0.162220  1.935118 -0.011814 -0.925369 -2.133829 -0.933173 -1.040196
314 -0.880874 -0.400956 -2.328729  0.380658  0.016809 -2.711706  1.361550
..        ...       ...       ...       ...       ...       ...       ...
775 -2.373011  1.748196 -1.426159  0.981177 -1.145867 -0.033774  0.340850
610 -0.587750  1.485507  0.022402  0.026463 -1.269696 -0.553590  1.054406
426 -0.510025  1.271371 -0.839987 -0.487718  0.846988 -0.539433 -0.390435
512 -1.207671  1.719265  1.811797  3.102993 -1.534562 -1.029973  0.170662
940  1.529456 -0.177965 -1.014976  0.360544 -0.351084 -1.262187  0.175720

           X0        X1
712 -0.815817 -0.636977
391  0.169036  0.171162
20   1.672974  0.514914
470 -1.040196 -0.933173
314  1.361550 -2.711706
..        ...       ...
775  0.340850 -0.033774
610  1.054406 -0.553590
426 -0.390435 -0.539433
512  0.170662 -1.029973
940  0.175720 -1.262187

[1000 rows x 9 columns], 'y': 712     6.780320
391    13.502571
20     14.509975
470    -0.021734
314    -3.811119
         ...
775    -7.934090
610     9.248446
426     8.947595
512     8.367137
940    12.297833
Name: y, Length: 1000, dtype: float64, 'treatment': 712     True
391     True
20      True
470     True
314    False
       ...
775    False
610     True
426     True
512     True
940     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    Gini   (Control):     0.7374
INFO:causalml:    Gini (Treatment):     0.9848
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0460
INFO:causalml:    RMSE (Treatment):     0.9925
INFO:causalml:   sMAPE   (Control):     0.5278
INFO:causalml:   sMAPE (Treatment):     0.1729
INFO:causalml:    Gini   (Control):     0.7357
INFO:causalml:    Gini (Treatment):     0.9855

{'X':            W4        W2        W1        W3        W0        X1        X0  \
574 -0.628170  0.490047 -0.998970  1.114067 -0.634946 -0.977042 -0.135971
279 -2.042524 -0.515422 -0.001494  0.585008 -0.007506  0.718590  1.220915
4   -1.318715  0.653079 -2.000279  0.498075 -0.156856 -0.883068  0.497926
346 -1.629199  0.177398 -0.054513  1.040503  0.789129 -0.768061  0.556015
311 -0.500132 -0.676690  0.496760 -0.867403 -1.887667  0.095543  1.170573
..        ...       ...       ...       ...       ...       ...       ...
165  0.701052 -0.293887  1.099465 -2.275733 -1.154126 -1.597081  0.433060
186 -0.713055  3.862460  0.200335  1.622113 -1.025908 -0.071922  0.803886
432 -1.883946  0.320996  0.350803  0.548764  0.239354 -0.240906  0.080076
363 -1.458807  1.554723 -0.100304 -0.726284 -0.183216 -2.651428  1.761662
256 -0.520119  1.788996 -0.945605  0.029491  0.376885 -0.323118  2.874166

           X0        X1
574 -0.135971 -0.977042
279  1.220915  0.718590
4    0.497926 -0.883068
346  0.556015 -0.768061
311  1.170573  0.095543
..        ...       ...
165  0.433060 -1.597081
186  0.803886 -0.071922
432  0.080076 -0.240906
363  1.761662 -2.651428
256  2.874166 -0.323118

[1000 rows x 9 columns], 'y': 574     5.633909
279    -6.155145
4      -4.253140
346    10.270986
311     6.548500
         ...
165     4.362892
186    14.738530
432     6.046513
363     8.937908
256    20.538593
Name: y, Length: 1000, dtype: float64, 'treatment': 574     True
279    False
4      False
346     True
311     True
       ...
165     True
186     True
432     True
363     True
256     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0604
INFO:causalml:    RMSE (Treatment):     0.9944
INFO:causalml:   sMAPE   (Control):     0.5353
INFO:causalml:   sMAPE (Treatment):     0.1628
INFO:causalml:    Gini   (Control):     0.7639
INFO:causalml:    Gini (Treatment):     0.9878
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1445
INFO:causalml:    RMSE (Treatment):     0.9943
INFO:causalml:   sMAPE   (Control):     0.5612
INFO:causalml:   sMAPE (Treatment):     0.1686
INFO:causalml:    Gini   (Control):     0.7414
INFO:causalml:    Gini (Treatment):     0.9876

{'X':            W4        W2        W1        W3        W0        X1        X0  \
589 -1.748176 -0.155602 -1.017759 -0.269662 -1.463838  0.574539 -0.268489
34   0.308690  2.301209  0.550368 -0.173328  0.312406  1.777416  1.357787
925 -0.454416 -0.506845 -1.768763  1.827165 -1.341544 -1.475720 -0.015125
500 -0.845036 -0.731265  0.645794  1.354525  1.237255 -0.111407  1.537868
646 -0.583674  1.079458  0.046224  1.641185 -1.743680 -0.669495  1.053700
..        ...       ...       ...       ...       ...       ...       ...
793 -0.493825  1.869213 -0.304250 -1.310964 -1.740684 -0.303164  1.395958
189  0.331711  0.200259  1.290837 -0.775730 -3.102590 -0.388644  0.719845
847 -0.727785  1.444691  0.778506  2.582550 -2.760931 -1.801513 -0.277971
585  0.992384  0.557539 -0.314856  0.502749  0.252605  0.635638  0.314569
557  0.221457 -0.260211  0.145958  1.385913 -1.584558 -1.443598 -0.270600

           X0        X1
589 -0.268489  0.574539
34   1.357787  1.777416
925 -0.015125 -1.475720
500  1.537868 -0.111407
646  1.053700 -0.669495
..        ...       ...
793  1.395958 -0.303164
189  0.719845 -0.388644
847 -0.277971 -1.801513
585  0.314569  0.635638
557 -0.270600 -1.443598

[1000 rows x 9 columns], 'y': 589   -10.258505
34     22.166660
925     4.053751
500    17.859054
646     9.399353
         ...
793     9.029191
189     5.702358
847     2.927082
585    17.683899
557     5.886610
Name: y, Length: 1000, dtype: float64, 'treatment': 589    False
34      True
925     True
500     True
646     True
       ...
793     True
189     True
847     True
585     True
557     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
670 -0.277250 -0.210543 -0.138522  0.102428  0.056937 -0.175638  0.013252
513 -0.744367  1.544602 -0.662760  2.360959  3.314790 -0.465990  0.294035
566 -0.642524  0.944299 -1.173029  0.761402 -0.130403 -1.047601  0.808957
82  -1.511905  0.757699 -0.695620  1.209082 -0.799840 -0.648411 -0.917466
720 -0.172043  0.454702 -1.152236 -1.934944 -1.645816  0.040783  2.268147
..        ...       ...       ...       ...       ...       ...       ...
624 -1.991574 -0.338454 -0.082311  1.218307 -1.216017  0.236149  1.025945
85  -1.707670  1.488772 -0.541956 -0.093565 -1.123841 -2.815247 -0.273263
802 -0.876361  0.328210  1.791160  1.911211 -1.427104 -1.932645  0.590992
256 -0.672850  1.743199 -0.845564  0.204138  0.135315 -0.465105  3.182018
686 -1.425466 -0.586366 -0.500467  2.146743 -1.612155  0.644111  0.201290

           X0        X1
670  0.013252 -0.175638
513  0.294035 -0.465990
566  0.808957 -1.047601
82  -0.917466 -0.648411
720  2.268147  0.040783
..        ...       ...
624  1.025945  0.236149
85  -0.273263 -2.815247
802  0.590992 -1.932645
256  3.182018 -0.465105
686  0.201290  0.644111

[1000 rows x 9 columns], 'y': 670     8.500958
513    22.411994
566    11.504404
82      0.633027
720    11.018665
         ...
624     5.810979
85     -0.384362
802     7.188003
256    20.538593
686     4.432389
Name: y, Length: 1000, dtype: float64, 'treatment': 670    True
513    True
566    True
82     True
720    True
       ...
624    True
85     True
802    True
256    True
686    True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0158
INFO:causalml:    RMSE (Treatment):     0.9613
INFO:causalml:   sMAPE   (Control):     0.5428
INFO:causalml:   sMAPE (Treatment):     0.1827
INFO:causalml:    Gini   (Control):     0.7311
INFO:causalml:    Gini (Treatment):     0.9887
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
294  0.652011  0.298007 -0.418953  0.292428 -0.732963 -1.633518  0.181559
839 -0.412169  0.833947 -0.603844  0.891018  0.087956 -0.819805  0.059602
397  0.740199  0.400858 -1.595990 -0.603329 -0.574721 -0.509353  0.696423
838 -0.740006 -0.058434 -0.927145  0.910512 -0.777889 -1.034288 -0.481204
473 -2.185995  3.010844  0.110059  0.565874 -1.589594 -2.013939  0.488150
..        ...       ...       ...       ...       ...       ...       ...
69  -1.483962  0.528791 -1.403685  1.730814 -1.962470  0.895590  2.864753
308 -1.362757  0.359206  0.455134  0.677304  1.099989 -0.654966  0.653793
585  0.879539  0.642240 -0.066938  0.411457 -0.063654  0.460095  0.641935
60   0.288490 -0.246400 -0.063626  1.811323 -1.230966  1.056647 -0.921207
878 -1.071925  2.714481 -2.157851  1.105897 -0.013140 -0.774942  0.812385

           X0        X1
294  0.181559 -1.633518
839  0.059602 -0.819805
397  0.696423 -0.509353
838 -0.481204 -1.034288
473  0.488150 -2.013939
..        ...       ...
69   2.864753  0.895590
308  0.653793 -0.654966
585  0.641935  0.460095
60  -0.921207  1.056647
878  0.812385 -0.774942

[1000 rows x 9 columns], 'y': 294     8.885443
839    10.718157
397    12.505105
838     3.145267
473     2.964870
         ...
69     14.310653
308    12.604981
585    17.683899
60      6.681242
878    11.079847
Name: y, Length: 1000, dtype: float64, 'treatment': 294    True
839    True
397    True
838    True
473    True
       ...
69     True
308    True
585    True
60     True
878    True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
528 -1.299357  2.085097 -0.391547  0.457171 -1.708455  0.655051  1.011679
304 -0.683685  0.803297  1.483699  2.148691  0.979045  1.007386  1.610230
681 -0.792822  1.511898  0.728455 -0.982430  1.296513 -0.075802  1.899100
578 -0.435146 -0.528440 -0.535245 -0.909243 -0.449900 -0.174179  0.636845
462 -1.007114  1.121916 -0.135373 -0.093987  0.835789 -0.091912 -0.269468
..        ...       ...       ...       ...       ...       ...       ...
292 -2.719957  1.122903 -0.402724  0.686720 -1.560917  0.788547  0.215067
610 -0.710158  1.637766  0.101329 -0.160697 -1.425310 -0.568631  1.076851
871 -1.592522 -0.440663 -2.352130 -0.994201 -0.403635 -0.702024 -0.783500
133 -2.033771  3.169595  0.556234  0.158380  1.204623  0.987751  1.142136
500 -0.779020 -0.811585  0.655207  1.303179  1.124806 -0.213754  1.466213

           X0        X1
528  1.011679  0.655051
304  1.610230  1.007386
681  1.899100 -0.075802
578  0.636845 -0.174179
462 -0.269468 -0.091912
..        ...       ...
292  0.215067  0.788547
610  1.076851 -0.568631
871 -0.783500 -0.702024
133  1.142136  0.987751
500  1.466213 -0.213754

[1000 rows x 9 columns], 'y': 528     8.156158
304    22.259686
681    18.090454
578    -2.946371
462     9.112148
         ...
292   -10.155301
610     9.248446
871    -8.791062
133    16.345538
500    17.859054
Name: y, Length: 1000, dtype: float64, 'treatment': 528     True
304     True
681     True
578    False
462     True
       ...
292    False
610     True
871    False
133     True
500     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1122
INFO:causalml:    RMSE (Treatment):     0.9994
INFO:causalml:   sMAPE   (Control):     0.5167
INFO:causalml:   sMAPE (Treatment):     0.1760
INFO:causalml:    Gini   (Control):     0.7523
INFO:causalml:    Gini (Treatment):     0.9873
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0266
INFO:causalml:    RMSE (Treatment):     1.0330
INFO:causalml:   sMAPE   (Control):     0.5590
INFO:causalml:   sMAPE (Treatment):     0.2011
INFO:causalml:    Gini   (Control):     0.7985
INFO:causalml:    Gini (Treatment):     0.9884
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9877
INFO:causalml:    RMSE (Treatment):     1.0373
INFO:causalml:   sMAPE   (Control):     0.5431
INFO:causalml:   sMAPE (Treatment):     0.1665
INFO:causalml:    Gini   (Control):     0.7361
INFO:causalml:    Gini (Treatment):     0.9850

{'X':            W4        W2        W1        W3        W0        X1        X0  \
72  -1.947483 -0.962961 -1.120901 -0.939085 -1.343606 -0.303198  0.063538
503 -2.046887  1.940449 -1.561164  0.401595  0.508324 -0.415936  1.012491
58  -0.709985  1.240568  0.630926 -0.459984 -0.993976 -0.669026  0.683581
857 -0.857882  0.661107 -0.166046 -1.069666 -0.415578 -0.026838 -0.309020
875 -2.420560  0.087914  0.299755  0.670579 -0.455537  0.289669 -0.663331
..        ...       ...       ...       ...       ...       ...       ...
101 -1.360162 -0.844073 -1.223409 -1.144797 -0.060488 -1.554531 -0.567665
505 -2.457799  0.798147  0.680603 -0.221115  0.916495 -1.643212  1.350440
1   -0.581011  0.661790 -1.094139  1.705350 -1.125393  0.910010  0.590032
965 -0.112124  2.114344 -1.426137  0.314121 -0.771549 -0.548006  3.069445
70  -0.733554  1.753741 -1.894252 -1.244993  0.873740 -0.934582  2.364144

           X0        X1
72   0.063538 -0.303198
503  1.012491 -0.415936
58   0.683581 -0.669026
857 -0.309020 -0.026838
875 -0.663331  0.289669
..        ...       ...
101 -0.567665 -1.554531
505  1.350440 -1.643212
1    0.590032  0.910010
965  3.069445 -0.548006
70   2.364144 -0.934582

[1000 rows x 9 columns], 'y': 72    -12.586391
503    -3.163402
58     -3.753323
857     4.361211
875     1.205333
         ...
101    -1.913847
505     8.711479
1      11.125450
965    19.735711
70     15.668385
Name: y, Length: 1000, dtype: float64, 'treatment': 72     False
503    False
58     False
857     True
875     True
       ...
101     True
505     True
1       True
965     True
70      True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
388 -1.737764  0.579226 -0.996179  0.571709  0.179947  0.265540  1.413841
721  0.151053 -0.186372 -2.078046  0.489469 -0.482097 -1.560702  0.955690
530 -0.970691 -0.230353 -1.122322  0.705896 -0.676484 -1.535906  1.770396
375 -1.706684 -0.608418 -0.324004 -0.367281 -0.953427 -1.326523  1.412785
795 -1.193762  1.727405 -1.310480 -0.078728  0.061590  0.338880 -0.823927
..        ...       ...       ...       ...       ...       ...       ...
784 -1.361975  0.925987 -1.238230  0.154941 -0.219589  2.112740  1.850467
706 -0.579411  2.152504  0.049398 -0.124011  0.829518 -0.821826  2.406820
452 -1.161144  1.447375  0.366940  1.502686 -0.832247 -2.464888  0.619286
250 -2.076800  2.575995 -3.396775  0.000262  0.176806 -0.916428 -0.759909
401 -1.900223  1.367527 -1.583811  1.389221 -0.270817  0.195192  0.439579

           X0        X1
388  1.413841  0.265540
721  0.955690 -1.560702
530  1.770396 -1.535906
375  1.412785 -1.326523
795 -0.823927  0.338880
..        ...       ...
784  1.850467  2.112740
706  2.406820 -0.821826
452  0.619286 -2.464888
250 -0.759909 -0.916428
401  0.439579  0.195192

[1000 rows x 9 columns], 'y': 388    -4.782170
721     9.936280
530    10.101463
375     3.740574
795     5.028396
         ...
784    14.827481
706    19.485830
452     7.577603
250     2.593009
401     7.798611
Name: y, Length: 1000, dtype: float64, 'treatment': 388    False
721     True
530     True
375     True
795     True
       ...
784     True
706     True
452     True
250     True
401     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9472
INFO:causalml:    RMSE (Treatment):     1.0280
INFO:causalml:   sMAPE   (Control):     0.5266
INFO:causalml:   sMAPE (Treatment):     0.1841
INFO:causalml:    Gini   (Control):     0.7202
INFO:causalml:    Gini (Treatment):     0.9863
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1008
INFO:causalml:    RMSE (Treatment):     1.0163
INFO:causalml:   sMAPE   (Control):     0.5614
INFO:causalml:   sMAPE (Treatment):     0.1746
INFO:causalml:    Gini   (Control):     0.7255
INFO:causalml:    Gini (Treatment):     0.9873

{'X':            W4        W2        W1        W3        W0        X1        X0  \
575 -1.604112  1.420476 -0.119304  2.071894 -1.150966 -0.098560 -1.312888
145 -2.738138  1.417467 -0.765806  0.714971  0.420035  0.129769  0.212606
801 -0.175570  1.353509 -0.480641 -0.622820  1.012828 -1.661558  0.661143
890 -1.229726  2.226561 -0.836272 -0.359287 -0.680700 -0.737701 -0.350118
338  0.185464  2.399160 -0.341294  0.318392  0.001034 -1.870320  1.383703
..        ...       ...       ...       ...       ...       ...       ...
87  -1.335835  1.166508 -2.310867  1.215919  0.564484  0.080287 -0.101429
396 -0.179289 -2.035594 -1.104917 -1.327889 -1.528992  0.540613  0.405879
535 -0.407648 -0.494758 -0.724853  0.480031 -0.544243 -1.247736  2.925960
853 -0.576296  1.875702 -0.953840 -2.121049  0.646644  0.852404  0.526995
122 -2.744112 -0.432944 -1.275036  0.830403  0.123720 -1.720654  0.565414

           X0        X1
575 -1.312888 -0.098560
145  0.212606  0.129769
801  0.661143 -1.661558
890 -0.350118 -0.737701
338  1.383703 -1.870320
..        ...       ...
87  -0.101429  0.080287
396  0.405879  0.540613
535  2.925960 -1.247736
853  0.526995  0.852404
122  0.565414 -1.720654

[1000 rows x 9 columns], 'y': 575     1.988673
145    -4.715614
801    13.424745
890    -3.809211
338    14.613827
         ...
87      8.347375
396    -9.105144
535    16.319324
853    11.211181
122    -8.281570
Name: y, Length: 1000, dtype: float64, 'treatment': 575     True
145    False
801     True
890    False
338     True
       ...
87      True
396    False
535     True
853     True
122    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
570 -0.251892  2.217378  0.128972  0.877789 -2.406003 -0.913432 -1.619846
138  0.573441  0.791258 -0.393677  1.597789 -1.053529  0.436214  0.005347
175 -1.239073  0.705045 -0.631863  0.820378  0.228619 -1.240018  2.829001
177 -2.041704  0.339386  1.887360  0.853861  0.079121 -1.425156  2.631870
507 -1.566631 -0.152512 -1.731438  0.974744 -1.418661 -1.855128  0.857973
..        ...       ...       ...       ...       ...       ...       ...
341 -1.380210  0.251500  2.155730 -1.460049 -0.465035 -1.399468  0.820430
882 -2.200841  0.473423 -1.663119 -0.534963  0.991635 -1.095692 -0.001438
52   0.272559  1.264574  0.920277  1.558250 -1.276324  0.140228 -0.269838
126 -0.180520  2.109796  0.497949 -0.657129 -0.339303 -1.753526  0.177609
191 -0.095375 -0.266770 -1.843198  0.164677 -1.451439  0.072266  0.011743

           X0        X1
570 -1.619846 -0.913432
138  0.005347  0.436214
175  2.829001 -1.240018
177  2.631870 -1.425156
507  0.857973 -1.855128
..        ...       ...
341  0.820430 -1.399468
882 -0.001438 -1.095692
52  -0.269838  0.140228
126  0.177609 -1.753526
191  0.011743  0.072266

[1000 rows x 9 columns], 'y': 570    -0.557158
138    11.274922
175    17.721185
177    13.887378
507    -8.258449
         ...
341     5.872196
882    -4.513230
52      9.973921
126     9.175909
191    -5.492128
Name: y, Length: 1000, dtype: float64, 'treatment': 570     True
138     True
175     True
177     True
507    False
       ...
341     True
882    False
52      True
126     True
191    False
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8388
INFO:causalml:    RMSE (Treatment):     0.9421
INFO:causalml:   sMAPE   (Control):     0.4387
INFO:causalml:   sMAPE (Treatment):     0.1767
INFO:causalml:    Gini   (Control):     0.7289
INFO:causalml:    Gini (Treatment):     0.9871
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True

{'X':            W4        W2        W1        W3        W0        X1        X0  \
674 -1.275184  1.358200 -0.726155 -0.106801  1.792994 -0.003869 -0.927698
170 -1.230840  2.762799 -1.489235  0.316969 -0.664481 -0.677436 -0.700865
364 -1.093882  1.445454  1.445245 -0.311255 -1.621083 -0.424070  1.006841
454 -0.414301  0.374911 -0.825270  0.696024 -1.912538 -0.252441  2.359192
1   -0.546745  0.560050 -1.029776  1.825449 -1.022233  0.862300  0.474211
..        ...       ...       ...       ...       ...       ...       ...
301 -0.716979 -0.937502  0.498046  0.779203 -0.393113 -1.010865  1.063551
209 -0.084613  0.739022 -1.232722 -0.059404  1.067833 -0.912770 -0.696018
139 -1.718548  0.936408  0.545828  0.020241  0.924205 -0.693409  1.116505
982 -0.800873  0.522693 -0.407466 -0.942755  0.556857 -1.488190  1.116656
353 -0.057680  1.006295 -0.067730 -0.207153 -1.318915  0.302234  2.503398

           X0        X1
674 -0.927698 -0.003869
170 -0.700865 -0.677436
364  1.006841 -0.424070
454  2.359192 -0.252441
1    0.474211  0.862300
..        ...       ...
301  1.063551 -1.010865
209 -0.696018 -0.912770
139  1.116505 -0.693409
982  1.116656 -1.488190
353  2.503398  0.302234

[1000 rows x 9 columns], 'y': 674     9.459822
170    -2.441764
364     7.693260
454    12.121477
1      11.125450
         ...
301     9.391349
209     8.004465
139    11.778033
982    12.003112
353    16.031327
Name: y, Length: 1000, dtype: float64, 'treatment': 674     True
170    False
364     True
454     True
1       True
       ...
301     True
209     True
139     True
982     True
353     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
996 -2.971085  1.947352 -0.453038  1.635119  1.145276  1.018412  2.208047
524 -0.145506  0.713185 -0.266433 -0.617454  0.114717 -0.278091  2.170591
450  0.200883  0.403165  0.126127  1.144775 -0.779232 -1.787856 -2.799749
697 -0.708518  0.657108 -1.175912  0.534101 -0.825237 -2.467847  0.026325
396 -0.268793 -2.167808 -1.130802 -1.329649 -1.476917  0.552037  0.543689
..        ...       ...       ...       ...       ...       ...       ...
370 -1.308630  0.430302 -1.112576  0.925546 -1.404983 -0.084853  0.940627
918 -1.811025  1.249892  0.812562  0.987837 -0.536020 -0.154597  1.555279
453 -1.161964 -0.627300 -0.312053  1.010994 -0.420745 -0.378774 -0.314639
196 -2.030011  1.175683 -1.086168 -0.637678 -1.431378 -1.200674  0.660302
73  -0.751418  0.109541 -0.967291  1.512617 -1.724470 -0.846512  3.628383

           X0        X1
996  2.208047  1.018412
524  2.170591 -0.278091
450 -2.799749 -1.787856
697  0.026325 -2.467847
396  0.543689  0.552037
..        ...       ...
370  0.940627 -0.084853
918  1.555279 -0.154597
453 -0.314639 -0.378774
196  0.660302 -1.200674
73   3.628383 -0.846512

[1000 rows x 9 columns], 'y': 996    17.211685
524    18.575304
450    -2.499583
697     3.459264
396    -9.105144
         ...
370     6.533270
918    12.845128
453     4.442757
196     1.676450
73     16.038810
Name: y, Length: 1000, dtype: float64, 'treatment': 996     True
524     True
450     True
697     True
396    False
       ...
370     True
918     True
453     True
196     True
73      True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    RMSE   (Control):     3.1302
INFO:causalml:    RMSE (Treatment):     0.9367
INFO:causalml:   sMAPE   (Control):     0.5797
INFO:causalml:   sMAPE (Treatment):     0.1733
INFO:causalml:    Gini   (Control):     0.7355
INFO:causalml:    Gini (Treatment):     0.9891
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0294
INFO:causalml:    RMSE (Treatment):     0.9617
INFO:causalml:   sMAPE   (Control):     0.5461
INFO:causalml:   sMAPE (Treatment):     0.1705
INFO:causalml:    Gini   (Control):     0.7490
INFO:causalml:    Gini (Treatment):     0.9879
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1241
INFO:causalml:    RMSE (Treatment):     1.0540
INFO:causalml:   sMAPE   (Control):     0.4882
INFO:causalml:   sMAPE (Treatment):     0.1936
INFO:causalml:    Gini   (Control):     0.7337
INFO:causalml:    Gini (Treatment):     0.9856

{'X':            W4        W2        W1        W3        W0        X1        X0  \
75  -0.757333 -0.712916  0.105189 -0.080202 -0.707051  1.598623  2.093872
102 -0.409235 -0.541200 -1.639509  1.664483  0.141387 -1.093047  0.767702
814  0.343624  2.428392 -0.183959  0.104039 -0.569434 -1.234335 -0.143349
709  0.787223  0.949325  1.536416  0.009398 -0.989808 -1.695050  1.432090
538 -1.220204  2.286013  0.177264  1.391621  1.819995 -2.184618  0.889050
..        ...       ...       ...       ...       ...       ...       ...
898 -1.313018  1.847124 -0.969984  1.714070  0.632784 -1.628770  2.241001
976  0.092232  0.422641 -0.635967 -0.206560  0.383167 -1.439808 -0.728771
432 -1.903219  0.642305  0.263048  0.563384  0.242865 -0.227973  0.052633
989 -1.398945  1.910521  2.088727  0.018377 -1.371115  0.390374  1.342492
534 -2.308233 -0.613270 -3.890913 -0.742875 -0.127011 -1.237730  0.209115

           X0        X1
75   2.093872  1.598623
102  0.767702 -1.093047
814 -0.143349 -1.234335
709  1.432090 -1.695050
538  0.889050 -2.184618
..        ...       ...
898  2.241001 -1.628770
976 -0.728771 -1.439808
432  0.052633 -0.227973
989  1.342492  0.390374
534  0.209115 -1.237730

[1000 rows x 9 columns], 'y': 75     -4.842126
102    12.024923
814     8.970856
709    14.547782
538    16.761939
         ...
898    17.331546
976     5.822048
432     6.046513
989    11.211139
534   -10.998817
Name: y, Length: 1000, dtype: float64, 'treatment': 75     False
102     True
814     True
709     True
538     True
       ...
898     True
976     True
432     True
989     True
534    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
325 -0.989731  0.780426 -0.471838  0.421229 -0.502973 -1.145870 -1.010960
816 -0.350574  0.965547 -0.381352 -0.638433 -2.333646 -1.730109 -0.276716
556 -1.366154  0.375266 -0.654065 -1.339450 -0.190017 -2.154392 -0.115430
769 -1.938171  0.734708 -0.308602 -0.203508 -0.249352  0.168703  1.681798
227 -0.762855  1.853916 -0.703500  1.230172  0.001538 -1.531708  0.454908
..        ...       ...       ...       ...       ...       ...       ...
536 -1.368469  0.234808 -0.581402 -0.272878 -0.204108  1.257436 -1.011244
585  0.915123  0.401585 -0.227586  0.532163 -0.063974  0.580786  0.861562
331 -1.142880  1.570995 -1.097257  0.688392  0.264632 -1.329392  0.596813
913 -1.821448  0.201364 -0.282816  2.146074  0.067142 -0.281985  0.904230
67  -0.957141  0.080458 -0.232750  0.945741 -0.507281 -2.479363 -1.488055

           X0        X1
325 -1.010960 -1.145870
816 -0.276716 -1.730109
556 -0.115430 -2.154392
769  1.681798  0.168703
227  0.454908 -1.531708
..        ...       ...
536 -1.011244  1.257436
585  0.861562  0.580786
331  0.596813 -1.329392
913  0.904230 -0.281985
67  -1.488055 -2.479363

[1000 rows x 9 columns], 'y': 325     1.470513
816    -0.020121
556     0.746452
769    -5.821681
227     9.555874
         ...
536     3.123954
585    17.683899
331    10.231377
913    10.638397
67     -0.972001
Name: y, Length: 1000, dtype: float64, 'treatment': 325     True
816     True
556     True
769    False
227     True
       ...
536     True
585     True
331     True
913     True
67      True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9356
INFO:causalml:    RMSE (Treatment):     0.9295
INFO:causalml:   sMAPE   (Control):     0.4843
INFO:causalml:   sMAPE (Treatment):     0.1740
INFO:causalml:    Gini   (Control):     0.7237
INFO:causalml:    Gini (Treatment):     0.9879
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8735
INFO:causalml:    RMSE (Treatment):     0.9463
INFO:causalml:   sMAPE   (Control):     0.4996
INFO:causalml:   sMAPE (Treatment):     0.2070
INFO:causalml:    Gini   (Control):     0.7439
INFO:causalml:    Gini (Treatment):     0.9887
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
864 -1.378966  1.163425 -1.222624 -1.350334 -1.365408 -0.717210  0.466981
87  -1.256415  1.284809 -2.247451  1.167455  0.624770 -0.131766  0.037739
735 -0.714745  0.352892 -2.026534 -0.469867  0.867790 -1.244862  2.108344
970 -1.482619  1.751589  1.381131 -0.789499 -0.669645 -1.595394 -0.017023
872 -0.693148  1.474590  0.515663 -1.858463  0.370774  0.060584  0.865481
..        ...       ...       ...       ...       ...       ...       ...
425 -0.549250  0.962562  0.366956  0.492343 -0.240984 -1.775844  1.552650
916 -2.362087  0.484083 -0.342292  0.332559  0.423326  0.218083 -0.177975
85  -1.540931  1.480379 -0.472548 -0.111599 -0.965064 -2.732768 -0.265116
357 -0.515377 -0.879357 -0.706456 -0.752520 -0.279704  0.325564 -0.580021
216 -1.361145  1.753494 -2.439841 -1.078344 -0.815397 -1.262698 -0.057730

           X0        X1
864  0.466981 -0.717210
87   0.037739 -0.131766
735  2.108344 -1.244862
970 -0.017023 -1.595394
872  0.865481  0.060584
..        ...       ...
425  1.552650 -1.775844
916 -0.177975  0.218083
85  -0.265116 -2.732768
357 -0.580021  0.325564
216 -0.057730 -1.262698

[1000 rows x 9 columns], 'y': 864    -8.539115
87      8.347375
735    15.212283
970     3.837965
872    11.901044
         ...
425    13.323890
916     4.529654
85     -0.384362
357     4.629609
216     2.376140
Name: y, Length: 1000, dtype: float64, 'treatment': 864    False
87      True
735     True
970     True
872     True
       ...
425     True
916     True
85      True
357     True
216     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
816 -0.388047  0.924588 -0.476783 -1.002072 -2.281807 -1.966699 -0.420618
275 -2.213287 -0.386113  0.142531  1.807303 -0.624429 -1.486348 -1.323718
999 -1.667723  0.404085 -0.014222  0.372220 -1.571434 -0.354246 -0.497524
404  0.251516 -1.381578 -0.688007  1.323307 -0.512810  0.842583 -0.338387
213 -0.847340  1.190321 -0.327402  1.416738 -1.672036 -0.540066  0.231177
..        ...       ...       ...       ...       ...       ...       ...
891 -0.268138 -0.445246 -0.878929  1.526581  0.791018  0.110399  1.971061
247 -1.091098  0.094910  0.672182  0.926585 -0.317620 -1.799480 -0.591769
762  0.248716  0.722264 -0.871655  0.522479 -0.891556  0.275079  2.329206
110 -1.976096  2.523392 -0.612924  1.400103 -0.539657  0.638363  0.916249
398 -0.408833  1.392336 -1.118775  0.410265 -1.701998 -1.404167  0.136762

           X0        X1
816 -0.420618 -1.966699
275 -1.323718 -1.486348
999 -0.497524 -0.354246
404 -0.338387  0.842583
213  0.231177 -0.540066
..        ...       ...
891  1.971061  0.110399
247 -0.591769 -1.799480
762  2.329206  0.275079
110  0.916249  0.638363
398  0.136762 -1.404167

[1000 rows x 9 columns], 'y': 816    -0.020121
275    -1.163599
999    -8.100697
404    -1.323791
213     5.306177
         ...
891    20.166272
247     3.320266
762    17.175897
110    11.934711
398    -5.105764
Name: y, Length: 1000, dtype: float64, 'treatment': 816     True
275     True
999    False
404    False
213     True
       ...
891     True
247     True
762     True
110     True
398    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
26   0.023678  0.058490  0.106981  2.666127 -0.511525 -1.991283  2.058464
631  0.511913  2.280563 -1.197253  1.562609 -0.215902 -1.020548  0.298896
591 -1.917706  1.021035  1.482307  0.291711 -0.439067 -0.077290 -0.568812
936 -0.469196 -1.072774 -0.445154 -0.658563 -0.924928 -2.700354  1.236708
221 -1.078034  0.828821 -1.093313 -0.510332 -2.243030  1.374379  0.571832
..        ...       ...       ...       ...       ...       ...       ...
231 -1.059865  1.882541 -0.203531  0.417511  1.029628 -1.972507  1.683474
45  -0.813366  0.618802  0.665742 -0.174151 -1.195950 -0.202764  1.213446
897 -0.522876 -0.893288 -1.382943  0.031426 -0.364126 -0.181860 -0.365989
79  -0.920979  0.756543  0.729420  0.835588 -0.316096 -0.910696  0.700811
338  0.143678  2.424775 -0.366023  0.065665 -0.229007 -1.673547  1.227384

           X0        X1
26   2.058464 -1.991283
631  0.298896 -1.020548
591 -0.568812 -0.077290
936  1.236708 -2.700354
221  0.571832  1.374379
..        ...       ...
231  1.683474 -1.972507
45   1.213446 -0.202764
897 -0.365989 -0.181860
79   0.700811 -0.910696
338  1.227384 -1.673547

[1000 rows x 9 columns], 'y': 26     17.468518
631    14.220450
591     3.407731
936     4.998172
221   -10.175373
         ...
231    16.734242
45      9.275797
897    -4.566144
79     10.632327
338    14.613827
Name: y, Length: 1000, dtype: float64, 'treatment': 26      True
631     True
591     True
936     True
221    False
       ...
231     True
45      True
897    False
79      True
338     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8528
INFO:causalml:    RMSE (Treatment):     0.9856
INFO:causalml:   sMAPE   (Control):     0.4977
INFO:causalml:   sMAPE (Treatment):     0.1629
INFO:causalml:    Gini   (Control):     0.7488
INFO:causalml:    Gini (Treatment):     0.9862
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.2241
INFO:causalml:    RMSE (Treatment):     0.9317
INFO:causalml:   sMAPE   (Control):     0.5776
INFO:causalml:   sMAPE (Treatment):     0.1543
INFO:causalml:    Gini   (Control):     0.6994
INFO:causalml:    Gini (Treatment):     0.9888
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
663  1.078179  0.156226  0.446981 -0.052989 -0.182723  0.688062  0.799450
959 -1.315478  1.172398 -1.866088  0.694998  0.951762  1.571519 -1.076170
542  0.662810  2.174263 -1.541615  1.123954 -0.612563  0.523472 -0.491306
213 -1.155594  1.235499 -0.292570  1.237619 -1.615249 -0.939958  0.070592
625 -1.321971  0.223750  0.478791 -0.019464  1.090621 -0.805170  1.259184
..        ...       ...       ...       ...       ...       ...       ...
9    0.192989  0.814800  1.424924 -1.145901 -0.763888  0.669395 -0.203615
19  -0.477455 -0.356164 -0.890055  2.177785 -1.006223 -1.615244  0.088047
830 -0.723356 -0.566580  0.276901  2.035714 -0.231138 -1.251536  1.154490
343 -1.264323  1.803949  0.915137 -1.755312 -0.898431 -0.865052  0.358412
818 -2.114619  0.956673  0.047994  0.222962 -2.378329 -0.680649  1.045604

           X0        X1
663  0.799450  0.688062
959 -1.076170  1.571519
542 -0.491306  0.523472
213  0.070592 -0.939958
625  1.259184 -0.805170
..        ...       ...
9   -0.203615  0.669395
19   0.088047 -1.615244
830  1.154490 -1.251536
343  0.358412 -0.865052
818  1.045604 -0.680649

[1000 rows x 9 columns], 'y': 663    15.384533
959     7.562645
542    12.169721
213     5.306177
625    12.619755
         ...
9       8.755592
19      6.700542
830    -1.062782
343     4.731928
818   -11.770683
Name: y, Length: 1000, dtype: float64, 'treatment': 663     True
959     True
542     True
213     True
625     True
       ...
9       True
19      True
830    False
343     True
818    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
835  0.306562 -0.032909 -0.403386 -1.002239  1.594554  0.150366  1.873825
630 -1.828867  0.706688 -0.076602 -1.285112  0.444461  1.199891  1.358834
770 -2.884794  0.052229 -1.146703 -0.461669 -1.249223  0.455205  0.867935
338  0.066256  2.294859 -0.250545  0.034695 -0.068221 -1.912920  1.239046
574 -0.511164  0.093076 -1.210577  1.056846 -0.482827 -1.004801 -0.190245
..        ...       ...       ...       ...       ...       ...       ...
336 -3.046083  1.587357  0.558987  1.342059 -0.764320 -2.113244  0.925285
95  -0.079302  4.714852  0.818300  0.292822 -0.515397 -2.804820 -0.486523
713 -0.536668  2.005241 -1.347787  1.364096 -0.445617 -0.789056 -0.185889
97  -0.694538 -0.132024  0.177391  0.442854  0.698154  0.802542  1.319311
375 -1.959415 -0.308475 -0.556457 -0.497601 -0.924967 -1.428880  1.441709

           X0        X1
835  1.873825  0.150366
630  1.358834  1.199891
770  0.867935  0.455205
338  1.239046 -1.912920
574 -0.190245 -1.004801
..        ...       ...
336  0.925285 -2.113244
95  -0.486523 -2.804820
713 -0.185889 -0.789056
97   1.319311  0.802542
375  1.441709 -1.428880

[1000 rows x 9 columns], 'y': 835    20.490073
630    11.111329
770   -12.067607
338    14.613827
574     5.633909
         ...
336     2.926169
95      9.600613
713     9.135870
97     16.738796
375     3.740574
Name: y, Length: 1000, dtype: float64, 'treatment': 835     True
630     True
770    False
338     True
574     True
       ...
336     True
95      True
713     True
97      True
375     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9748
INFO:causalml:    RMSE (Treatment):     1.0791
INFO:causalml:   sMAPE   (Control):     0.5030
INFO:causalml:   sMAPE (Treatment):     0.1791
INFO:causalml:    Gini   (Control):     0.7130
INFO:causalml:    Gini (Treatment):     0.9842
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8029
INFO:causalml:    RMSE (Treatment):     0.9547
INFO:causalml:   sMAPE   (Control):     0.5077
INFO:causalml:   sMAPE (Treatment):     0.1519
INFO:causalml:    Gini   (Control):     0.7321
INFO:causalml:    Gini (Treatment):     0.9868
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9671
INFO:causalml:    RMSE (Treatment):     1.0479

{'X':            W4        W2        W1        W3        W0        X1        X0  \
349 -1.721742  0.994178 -0.248258  0.170946  0.084045 -0.579222  1.403727
348 -1.015222  2.066811 -1.843225  0.417722 -1.444937 -1.728400  1.654515
243 -3.048965 -0.518166  0.249463  1.413264 -1.336714 -0.364473  0.627325
561 -0.585552 -0.209026 -0.357597  1.027069 -2.551457  0.321784  1.257139
445 -0.080532  0.021305  0.997780  2.976259 -0.609740 -0.540314  1.162446
..        ...       ...       ...       ...       ...       ...       ...
898 -1.390026  1.825700 -0.988322  1.716423  0.559838 -1.567219  2.137330
563 -0.466899 -0.165808 -3.015552  1.193963 -1.463896  1.092221  0.453459
573  0.212725  1.077798 -1.054672  1.956676 -1.734482 -1.164064 -1.007024
315 -0.646043  0.204124  1.405069  1.427420  0.253353 -0.779818  0.430896
416 -1.034436  0.322248 -0.198104 -0.459426 -0.633344 -1.344082 -0.073182

           X0        X1
349  1.403727 -0.579222
348  1.654515 -1.728400
243  0.627325 -0.364473
561  1.257139  0.321784
445  1.162446 -0.540314
..        ...       ...
898  2.137330 -1.567219
563  0.453459  1.092221
573 -1.007024 -1.164064
315  0.430896 -0.779818
416 -0.073182 -1.344082

[1000 rows x 9 columns], 'y': 349    10.315484
348     9.469476
243   -10.483545
561     8.271140
445    16.120442
         ...
898    17.331546
563    -5.142521
573     3.364299
315    11.545213
416     2.647910
Name: y, Length: 1000, dtype: float64, 'treatment': 349     True
348     True
243    False
561     True
445     True
       ...
898     True
563    False
573     True
315     True
416     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
177 -1.922237  0.485077  1.631578  0.819498  0.107530 -1.224345  2.252789
248 -0.342131  0.605659 -1.673302  0.453481  1.089323 -0.869957 -0.334178
519 -1.082507  1.630724 -0.037752  0.137577 -1.415357 -0.872408  0.516459
54   1.568927  2.117927  1.208255  0.531668 -1.185251 -0.768777 -0.675083
993 -0.932022  0.529658 -0.377867  0.116624 -0.725836  0.124112  1.236988
..        ...       ...       ...       ...       ...       ...       ...
698 -1.138849  1.739492  0.295478  1.372403 -1.312977  0.210428  0.045664
135 -1.665508  2.299056 -1.063216  1.214164 -3.322477 -0.037896  0.924240
681 -0.842081  1.444749  0.741345 -0.852470  1.257753 -0.079214  1.672446
535 -0.646823 -0.444539 -0.586156  0.504034 -0.294741 -1.590632  3.031652
135 -1.574404  2.295451 -1.082528  1.400681 -3.205474  0.187770  0.662262

           X0        X1
177  2.252789 -1.224345
248 -0.334178 -0.869957
519  0.516459 -0.872408
54  -0.675083 -0.768777
993  1.236988  0.124112
..        ...       ...
698  0.045664  0.210428
135  0.924240 -0.037896
681  1.672446 -0.079214
535  3.031652 -1.590632
135  0.662262  0.187770

[1000 rows x 9 columns], 'y': 177    13.887378
248    10.511960
519     6.513308
54      9.737109
993    10.904694
         ...
698     7.381116
135     2.418843
681    18.090454
535    16.319324
135     2.418843
Name: y, Length: 1000, dtype: float64, 'treatment': 177    True
248    True
519    True
54     True
993    True
       ...
698    True
135    True
681    True
535    True
135    True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:   sMAPE   (Control):     0.5210
INFO:causalml:   sMAPE (Treatment):     0.1817
INFO:causalml:    Gini   (Control):     0.7762
INFO:causalml:    Gini (Treatment):     0.9860
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8423
INFO:causalml:    RMSE (Treatment):     0.9524
INFO:causalml:   sMAPE   (Control):     0.4624
INFO:causalml:   sMAPE (Treatment):     0.1790
INFO:causalml:    Gini   (Control):     0.7398
INFO:causalml:    Gini (Treatment):     0.9876
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9004
INFO:causalml:    RMSE (Treatment):     0.9385
INFO:causalml:   sMAPE   (Control):     0.4947
INFO:causalml:   sMAPE (Treatment):     0.1545
INFO:causalml:    Gini   (Control):     0.7617
INFO:causalml:    Gini (Treatment):     0.9878

{'X':            W4        W2        W1        W3        W0        X1        X0  \
724 -1.438144 -0.332737  0.392911  0.212140 -0.665798  0.343149  0.757040
751 -0.401081 -0.175121 -0.010361 -0.048220 -0.675733 -1.009049  1.318978
687  0.182106  0.577860 -1.602666  1.575414 -0.252950 -0.201800  2.081642
294  0.691661  0.305707 -0.276849  0.436226 -0.938440 -1.498923 -0.104459
100 -1.136832  1.936971 -0.818190  2.375104 -0.480383 -1.567064  1.095914
..        ...       ...       ...       ...       ...       ...       ...
311 -0.539638 -0.734239  0.330032 -0.759054 -1.973271 -0.025732  1.392467
92  -1.274988  0.222087 -0.171338  0.406686  0.299645 -1.318381  0.211155
280 -0.980919 -0.636800 -0.822122 -0.528152 -0.459369 -1.805989  1.034790
948  0.160932  0.722128  0.042512 -0.375004 -1.753827 -0.357351  0.838413
668 -0.174736  0.461147  0.483940 -0.073567 -1.070415 -0.771726  1.044764

           X0        X1
724  0.757040  0.343149
751  1.318978 -1.009049
687  2.081642 -0.201800
294 -0.104459 -1.498923
100  1.095914 -1.567064
..        ...       ...
311  1.392467 -0.025732
92   0.211155 -1.318381
280  1.034790 -1.805989
948  0.838413 -0.357351
668  1.044764 -0.771726

[1000 rows x 9 columns], 'y': 724     7.856564
751    10.177478
687    19.701253
294     8.885443
100    11.113321
         ...
311     6.548500
92      7.646588
280     5.994137
948     9.814477
668    10.579344
Name: y, Length: 1000, dtype: float64, 'treatment': 724    True
751    True
687    True
294    True
100    True
       ...
311    True
92     True
280    True
948    True
668    True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
51  -0.271968  0.458390  1.267380 -0.199087 -0.197657 -0.538826 -0.226384
10   0.970625  3.067773  0.223898  2.879977  0.834710 -1.322450  1.055600
975 -0.912818  0.808119  0.283279  1.816216 -0.525104 -1.245342  0.897152
290  0.454061 -1.064971  0.315819 -0.383260  0.775556 -1.332200  0.745180
642 -1.271036  0.321130 -1.184572  0.813957 -0.373138  0.408561 -0.441891
..        ...       ...       ...       ...       ...       ...       ...
23  -1.745951  0.284976 -0.584939  0.009987  0.281503  0.464791  2.089986
905 -1.137892  0.810218  0.339671 -0.323423 -0.825219 -1.534036  0.640463
216 -1.098435  1.733659 -2.455863 -0.949506 -0.900628 -1.484929 -0.096884
28   0.519413 -1.793534 -0.629775 -1.531616 -0.133148 -0.455310 -0.300663
741 -0.663581  2.882579 -1.455952 -0.962903 -1.235194 -0.395429  0.136463

           X0        X1
51  -0.226384 -0.538826
10   1.055600 -1.322450
975  0.897152 -1.245342
290  0.745180 -1.332200
642 -0.441891  0.408561
..        ...       ...
23   2.089986  0.464791
905  0.640463 -1.534036
216 -0.096884 -1.484929
28  -0.300663 -0.455310
741  0.136463 -0.395429

[1000 rows x 9 columns], 'y': 51      8.374283
10     24.500212
975     9.860888
290    12.664366
642     4.413860
         ...
23     -4.601417
905    -5.143310
216     2.376140
28      4.753158
741     5.593102
Name: y, Length: 1000, dtype: float64, 'treatment': 51      True
10      True
975     True
290     True
642     True
       ...
23     False
905    False
216     True
28      True
741     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9230
INFO:causalml:    RMSE (Treatment):     0.9689
INFO:causalml:   sMAPE   (Control):     0.5764
INFO:causalml:   sMAPE (Treatment):     0.1908
INFO:causalml:    Gini   (Control):     0.7373
INFO:causalml:    Gini (Treatment):     0.9894
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9404
INFO:causalml:    RMSE (Treatment):     0.9361
INFO:causalml:   sMAPE   (Control):     0.5608

{'X':            W4        W2        W1        W3        W0        X1        X0  \
151 -1.451343  2.117412  0.057350  0.228720 -0.666035  0.318744 -0.507604
289  0.709237 -0.097306 -0.534428  0.454660  1.091187 -1.461089  1.641729
856 -2.090114  0.977529 -2.008539 -0.850157 -2.154341 -2.185380 -0.444760
507 -1.498719 -0.280197 -1.843650  1.012497 -1.428442 -2.318102  0.960008
593 -0.254514  1.059549 -0.632799 -1.430683 -0.380345 -0.097567  0.396568
..        ...       ...       ...       ...       ...       ...       ...
188  1.981869  1.294382 -0.672061  1.002822 -0.906319  0.110632  0.519760
274 -1.441295  1.166923 -1.110185  1.572215 -1.119379 -0.864990  0.912239
688 -0.547513  0.834360 -1.085616 -0.343214 -1.414722 -2.185953  0.446973
173 -1.768526  0.219728 -1.886426  1.139153 -1.269672 -1.316015  0.551071
365 -0.013680 -0.244481  0.764536  0.988104  0.414957 -1.574025  1.368041

           X0        X1
151 -0.507604  0.318744
289  1.641729 -1.461089
856 -0.444760 -2.185380
507  0.960008 -2.318102
593  0.396568 -0.097567
..        ...       ...
188  0.519760  0.110632
274  0.912239 -0.864990
688  0.446973 -2.185953
173  0.551071 -1.316015
365  1.368041 -1.574025

[1000 rows x 9 columns], 'y': 151     4.957949
289    19.767563
856   -13.300039
507    -8.258449
593     8.505994
         ...
188    17.138200
274    -4.605752
688    -5.466406
173    -7.857862
365    15.195618
Name: y, Length: 1000, dtype: float64, 'treatment': 151     True
289     True
856    False
507    False
593     True
       ...
188     True
274    False
688    False
173    False
365     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
255  1.133141  2.240067  1.125542 -0.273125 -0.989053 -0.952641  0.326860
574 -0.627392  0.302338 -0.988008  1.136665 -0.486729 -1.072095 -0.240808
571  0.036812 -0.336157  1.466677  0.344865  0.441510 -0.684800  1.410161
914  0.028245  1.206804  0.219451 -0.591808 -0.976701  0.822046  0.977400
602 -2.005434  2.564907 -1.005840  1.727996 -1.563756  0.382768 -1.360408
..        ...       ...       ...       ...       ...       ...       ...
835  0.087010  0.143228 -0.435516 -1.147568  1.888092  0.346188  1.742445
622 -0.937723 -0.820100  0.087610 -0.325510  0.338565 -0.135640  0.278474
425 -0.625617  0.946650  0.343589  0.381559 -0.167683 -1.652701  1.941279
365 -0.018941 -0.296919  0.643877  1.094566  0.281830 -1.407360  1.414953
657 -0.973794  0.735844  0.302811  0.673145  0.283865 -0.848070  0.960780

           X0        X1
255  0.326860 -0.952641
574 -0.240808 -1.072095
571  1.410161 -0.684800
914  0.977400  0.822046
602 -1.360408  0.382768
..        ...       ...
835  1.742445  0.346188
622  0.278474 -0.135640
425  1.941279 -1.652701
365  1.414953 -1.407360
657  0.960780 -0.848070

[1000 rows x 9 columns], 'y': 255    13.881950
574     5.633909
571    16.753939
914    12.801840
602     1.254728
         ...
835    20.490073
622     8.845133
425    13.323890
365    15.195618
657    12.117853
Name: y, Length: 1000, dtype: float64, 'treatment': 255    True
574    True
571    True
914    True
602    True
       ...
835    True
622    True
425    True
365    True
657    True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:   sMAPE (Treatment):     0.1717
INFO:causalml:    Gini   (Control):     0.7444
INFO:causalml:    Gini (Treatment):     0.9889
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0033
INFO:causalml:    RMSE (Treatment):     0.9767
INFO:causalml:   sMAPE   (Control):     0.4587
INFO:causalml:   sMAPE (Treatment):     0.1761
INFO:causalml:    Gini   (Control):     0.7124
INFO:causalml:    Gini (Treatment):     0.9873
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9830
INFO:causalml:    RMSE (Treatment):     1.0151
INFO:causalml:   sMAPE   (Control):     0.4974

{'X':            W4        W2        W1        W3        W0        X1        X0  \
57  -0.241496  0.341353 -0.879670  0.653348  0.562012 -0.874722  2.321090
946 -0.039226  1.070254  0.817258  1.531369 -0.729411 -1.606529  1.682653
779 -1.350659  0.815369  0.127656  0.103901  0.278969 -0.815212  0.066001
896 -2.432024 -0.896851  1.092218  0.425996 -1.353030 -0.393840 -1.777303
403 -1.548241  2.346455  0.703124  0.469986 -2.174662 -0.597804 -1.659799
..        ...       ...       ...       ...       ...       ...       ...
500 -0.764798 -0.741966  0.661109  1.471012  1.249719 -0.250612  1.784505
353 -0.129342  0.937341 -0.341557  0.065221 -1.231137  0.286639  2.308218
100 -1.378639  1.891778 -0.944875  2.350588 -0.498995 -1.187752  0.950568
384 -0.985811  0.428972 -1.376992 -0.276737 -0.658336  0.236872  0.755765
427  0.428906  0.044837  0.574607  1.415539 -1.691916 -0.202051  1.901581

           X0        X1
57   2.321090 -0.874722
946  1.682653 -1.606529
779  0.066001 -0.815212
896 -1.777303 -0.393840
403 -1.659799 -0.597804
..        ...       ...
500  1.784505 -0.250612
353  2.308218  0.286639
100  0.950568 -1.187752
384  0.755765  0.236872
427  1.901581 -0.202051

[1000 rows x 9 columns], 'y': 57     18.788397
946    16.526801
779     7.001850
896    -7.004058
403    -3.265557
         ...
500    17.859054
353    16.031327
100    11.113321
384    -5.168611
427    15.122153
Name: y, Length: 1000, dtype: float64, 'treatment': 57      True
946     True
779     True
896     True
403     True
       ...
500     True
353     True
100     True
384    False
427     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
882 -2.069892  0.626586 -1.832899 -0.427297  1.053283 -0.782349 -0.035057
365 -0.010483 -0.356730  0.588927  1.041733  0.428563 -1.548626  1.165125
645  0.895264  1.665214 -0.241590 -0.355268 -2.385213 -1.247615  0.714368
183 -1.739849  0.790751  0.101503 -0.955607  0.571522  0.000875  0.226301
160 -1.739012  2.249436  0.471353  1.260166  0.195565 -0.286865  1.957172
..        ...       ...       ...       ...       ...       ...       ...
559 -1.348577 -0.508567  0.135080  0.760634 -0.578021 -1.684832  0.011024
536 -1.320728  0.180165 -0.598624 -0.357499 -0.200479  1.283381 -0.942567
387 -1.085304  1.898855 -0.049487 -0.493745  1.112310 -1.294861  2.878083
652 -1.434432 -0.515402 -2.014047  0.739681  0.447348  0.527605 -0.378463
466  0.169418  1.495641 -0.416136  1.897580 -0.623185  0.534709  1.641410

           X0        X1
882 -0.035057 -0.782349
365  1.165125 -1.548626
645  0.714368 -1.247615
183  0.226301  0.000875
160  1.957172 -0.286865
..        ...       ...
559  0.011024 -1.684832
536 -0.942567  1.283381
387  2.878083 -1.294861
652 -0.378463  0.527605
466  1.641410  0.534709

[1000 rows x 9 columns], 'y': 882    -4.513230
365    15.195618
645     8.189153
183     5.804148
160    15.240536
         ...
559     2.992068
536     3.123954
387    19.096949
652    -3.763410
466    18.638368
Name: y, Length: 1000, dtype: float64, 'treatment': 882    False
365     True
645     True
183     True
160     True
       ...
559     True
536     True
387     True
652    False
466     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:   sMAPE (Treatment):     0.1793
INFO:causalml:    Gini   (Control):     0.7089
INFO:causalml:    Gini (Treatment):     0.9869
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1606
INFO:causalml:    RMSE (Treatment):     0.9664
INFO:causalml:   sMAPE   (Control):     0.5522
INFO:causalml:   sMAPE (Treatment):     0.1688
INFO:causalml:    Gini   (Control):     0.6994
INFO:causalml:    Gini (Treatment):     0.9879
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
62  -0.489888  0.421805 -3.885209 -1.142352 -0.661527 -1.367046  0.190653
701 -0.397797 -1.049307 -0.657659  0.561405 -0.665239  0.960965  1.657074
646 -0.505355  1.022138  0.422064  1.678294 -1.630333 -0.457794  0.460388
698 -1.127903  1.739689  0.090811  1.239873 -1.455543  0.148345 -0.075825
320 -1.348275  1.640355  0.014880 -0.332567 -0.476067  0.113882  1.460478
..        ...       ...       ...       ...       ...       ...       ...
671 -1.830457  1.927874  0.108815  0.686607 -1.445390 -0.412422 -0.739515
227 -0.617478  2.117264 -0.796843  1.345096  0.100885 -1.472998  0.194239
832 -0.635179  1.771168 -0.200905 -0.479498 -0.068345 -1.282431  0.163844
170 -1.176175  2.724439 -1.543883  0.238433 -0.518789 -0.637966 -0.519854
715 -0.437745  0.651706 -0.292567 -1.485070 -1.177736 -0.601624 -0.962816

           X0        X1
62   0.190653 -1.367046
701  1.657074  0.960965
646  0.460388 -0.457794
698 -0.075825  0.148345
320  1.460478  0.113882
..        ...       ...
671 -0.739515 -0.412422
227  0.194239 -1.472998
832  0.163844 -1.282431
170 -0.519854 -0.637966
715 -0.962816 -0.601624

[1000 rows x 9 columns], 'y': 62     -5.979631
701    13.534369
646     9.399353
698     7.381116
320    11.942735
         ...
671    -6.150284
227     9.555874
832     7.911675
170    -2.441764
715     0.458874
Name: y, Length: 1000, dtype: float64, 'treatment': 62     False
701     True
646     True
698     True
320     True
       ...
671    False
227     True
832     True
170    False
715     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
173 -1.890919  0.303230 -1.853986  1.086205 -1.063154 -1.480039  0.557782
452 -0.908609  1.446287  0.602898  1.273491 -0.728457 -2.722508  0.638021
362 -2.772817  1.633125  0.477258 -1.222814 -1.376002 -1.625118  0.168268
856 -2.213905  1.100170 -1.873703 -0.831352 -2.196694 -2.148791 -0.410641
778 -2.232317 -0.354925  0.024290  0.391985  0.347478 -1.752844  0.687425
..        ...       ...       ...       ...       ...       ...       ...
648  0.687883 -0.708345  0.617329 -1.582533 -2.457736 -1.103158  0.881376
931 -0.749425  0.972833 -0.048579 -0.511019 -0.159517 -1.135516  0.958728
202 -1.547532  1.893259  1.899565  1.088557 -0.511950 -1.168168 -1.399601
314 -0.929180 -0.435418 -2.500459  0.362450  0.110227 -2.574999  1.657417
885 -2.073608 -0.503616 -1.645188  0.427103 -1.614990  0.502726  0.944974

           X0        X1
173  0.557782 -1.480039
452  0.638021 -2.722508
362  0.168268 -1.625118
856 -0.410641 -2.148791
778  0.687425 -1.752844
..        ...       ...
648  0.881376 -1.103158
931  0.958728 -1.135516
202 -1.399601 -1.168168
314  1.657417 -2.574999
885  0.944974  0.502726

[1000 rows x 9 columns], 'y': 173    -7.857862
452     7.577603
362    -3.054046
856   -13.300039
778     6.019405
         ...
648     4.759991
931    10.080089
202     2.297590
314    -3.811119
885   -11.199742
Name: y, Length: 1000, dtype: float64, 'treatment': 173    False
452     True
362     True
856    False
778     True
       ...
648     True
931     True
202     True
314    False
885    False
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8703
INFO:causalml:    RMSE (Treatment):     0.9484
INFO:causalml:   sMAPE   (Control):     0.4835
INFO:causalml:   sMAPE (Treatment):     0.1689
INFO:causalml:    Gini   (Control):     0.7614
INFO:causalml:    Gini (Treatment):     0.9884
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0918
INFO:causalml:    RMSE (Treatment):     1.0446
INFO:causalml:   sMAPE   (Control):     0.4937
INFO:causalml:   sMAPE (Treatment):     0.1971
INFO:causalml:    Gini   (Control):     0.7099
INFO:causalml:    Gini (Treatment):     0.9853
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9127
INFO:causalml:    RMSE (Treatment):     0.8881
INFO:causalml:   sMAPE   (Control):     0.5185
INFO:causalml:   sMAPE (Treatment):     0.1651

{'X':            W4        W2        W1        W3        W0        X1        X0  \
229 -1.693449  0.975769  0.294856  1.677213  0.570499  0.236995  3.001468
32  -1.099286  0.898796  0.284389 -0.614014 -1.150040  0.589624  0.196437
407 -1.199841  0.583590 -0.114395 -0.810430  0.316471  1.190869  0.694040
883 -1.139762  0.160852 -0.071674 -0.104197 -1.540943  0.839832  0.628072
155  0.186297  2.211595  0.272060  1.710993 -0.343155  0.940461  1.044007
..        ...       ...       ...       ...       ...       ...       ...
884 -0.206463  2.515624 -0.597811  0.358788 -1.639772 -1.142756  0.578135
107 -1.391807  0.824917 -0.114520  0.917218 -0.346789 -0.623044  1.090865
345 -1.194521  2.556273  0.304178 -0.438781 -0.127703 -0.705603  1.153798
174 -1.389069  2.427555 -1.962768  0.380924 -2.720164 -0.777716 -0.018113
132 -4.057538  1.581352 -1.081639  1.262583 -2.059606  0.227712  0.373265

           X0        X1
229  3.001468  0.236995
32   0.196437  0.589624
407  0.694040  1.190869
883  0.628072  0.839832
155  1.044007  0.940461
..        ...       ...
884  0.578135 -1.142756
107  1.090865 -0.623044
345  1.153798 -0.705603
174 -0.018113 -0.777716
132  0.373265  0.227712

[1000 rows x 9 columns], 'y': 229    21.267379
32     -5.941016
407    10.716455
883     5.901248
155    19.147114
         ...
884     9.392570
107    -2.840767
345    12.057991
174     1.619585
132    -3.629107
Name: y, Length: 1000, dtype: float64, 'treatment': 229     True
32     False
407     True
883     True
155     True
       ...
884     True
107    False
345     True
174     True
132     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
486 -0.060378 -0.386603 -0.624311  0.427205  0.865676 -1.184077  0.559674
584 -0.754149  0.524564  0.259186  0.327950  0.764248  1.517739 -0.343597
393 -1.303196  0.605609  0.168569  1.115444 -0.924550 -0.102834  0.081622
715 -0.612551  0.682669 -0.199036 -1.235912 -1.123797 -0.718232 -0.907246
287 -2.030588 -0.724655 -2.152445  1.455423 -1.699869  0.777392  0.490297
..        ...       ...       ...       ...       ...       ...       ...
138  0.611603  0.658839 -0.438636  1.626230 -1.021618  0.334653 -0.099974
70  -0.565836  1.834783 -2.122074 -1.142553  0.905590 -0.660720  1.852484
723  0.034264  1.444662  0.785691  0.755711 -0.643581 -0.377432  0.660484
538 -0.823910  2.479396  0.152882  1.342818  1.880938 -1.872310  0.653014
697 -0.914739  0.515895 -1.237094  0.455332 -0.697591 -2.569362 -0.049337

           X0        X1
486  0.559674 -1.184077
584 -0.343597  1.517739
393  0.081622 -0.102834
715 -0.907246 -0.718232
287  0.490297  0.777392
..        ...       ...
138 -0.099974  0.334653
70   1.852484 -0.660720
723  0.660484 -0.377432
538  0.653014 -1.872310
697 -0.049337 -2.569362

[1000 rows x 9 columns], 'y': 486    12.711964
584    11.093724
393    -4.149152
715     0.458874
287   -11.017227
         ...
138    11.274922
70     15.668385
723    12.592456
538    16.761939
697     3.459264
Name: y, Length: 1000, dtype: float64, 'treatment': 486     True
584     True
393    False
715     True
287    False
       ...
138     True
70      True
723     True
538     True
697     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    Gini   (Control):     0.6943
INFO:causalml:    Gini (Treatment):     0.9893
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9119
INFO:causalml:    RMSE (Treatment):     1.1321

{'X':            W4        W2        W1        W3        W0        X1        X0  \
498  0.925632  1.248556  0.266050  0.624337  0.139784  0.054055  0.611980
343 -1.275755  1.853760  0.893049 -1.703371 -0.923133 -1.113825  0.470165
406 -1.723518  0.869494 -0.410107 -0.411069  0.242774  1.029431  0.633780
347 -0.783681 -0.425386 -1.016921  1.284220 -1.134495 -0.170870 -0.188672
107 -1.302936  0.849902 -0.159648  0.948353 -0.246683 -0.672936  0.827165
..        ...       ...       ...       ...       ...       ...       ...
650 -1.089455  0.154798 -2.850727  1.223669  0.311584 -1.655243  0.105204
109 -1.315574  0.194043 -0.454507  0.856102  1.277240 -0.215829  2.329545
329 -0.763798  0.152340 -0.132608  0.221868 -1.791237  0.079281  2.224855
960  0.161576 -0.287115  0.066753 -0.917243 -2.944296 -0.165028  1.221761
106 -1.481454  1.547463  0.125611 -0.352701 -2.068552 -1.862149  0.736369

           X0        X1
498  0.611980  0.054055
343  0.470165 -1.113825
406  0.633780  1.029431
347 -0.188672 -0.170870
107  0.827165 -0.672936
..        ...       ...
650  0.105204 -1.655243
109  2.329545 -0.215829
329  2.224855  0.079281
960  1.221761 -0.165028
106  0.736369 -1.862149

[1000 rows x 9 columns], 'y': 498    17.581110
343     4.731928
406     9.585905
347     4.085427
107    -2.840767
         ...
650    -2.393578
109    18.593730
329    10.777317
960     4.643805
106    -8.631314
Name: y, Length: 1000, dtype: float64, 'treatment': 498     True
343     True
406     True
347     True
107    False
       ...
650    False
109     True
329     True
960     True
106    False
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:   sMAPE   (Control):     0.5824
INFO:causalml:   sMAPE (Treatment):     0.1933
INFO:causalml:    Gini   (Control):     0.7839
INFO:causalml:    Gini (Treatment):     0.9837
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0786
INFO:causalml:    RMSE (Treatment):     0.9618
INFO:causalml:   sMAPE   (Control):     0.5333
INFO:causalml:   sMAPE (Treatment):     0.1850
INFO:causalml:    Gini   (Control):     0.7419
INFO:causalml:    Gini (Treatment):     0.9893
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
181  1.125728  0.140116  0.069322  0.831179 -0.385813 -0.950129  0.420018
422 -1.770041  0.841889 -0.224366 -0.392414  2.191028  0.024989  0.319091
659 -0.723129  2.909802 -0.962345  2.917851 -1.736066 -0.935685 -0.665218
51  -0.289412  0.743801  1.214467 -0.131556 -0.254289 -0.561595 -0.126134
684 -2.040331  2.247077  0.239600  0.453179  1.300511 -0.708659  0.690296
..        ...       ...       ...       ...       ...       ...       ...
874  0.536244  2.342032  0.843026  2.121612 -1.791246 -0.603460  1.230203
146 -0.789941 -0.290262 -1.449763  0.313378  0.341513 -0.942170 -1.177943
162 -1.414215  0.924055 -0.993010  0.433887  1.924170 -2.278847  2.402717
972 -0.083595  0.625131 -0.987192  1.608500  1.194546 -1.260879 -0.750448
426 -0.539558  1.140058 -0.611717 -0.200436  0.854985 -0.856433 -0.357662

           X0        X1
181  0.420018 -0.950129
422  0.319091  0.024989
659 -0.665218 -0.935685
51  -0.126134 -0.561595
684  0.690296 -0.708659
..        ...       ...
874  1.230203 -0.603460
146 -1.177943 -0.942170
162  2.402717 -2.278847
972 -0.750448 -1.260879
426 -0.357662 -0.856433

[1000 rows x 9 columns], 'y': 181    13.569762
422    11.141529
659     4.772285
51      8.374283
684    11.451222
         ...
874    16.052226
146     3.922772
162    17.934588
972    10.333966
426     8.947595
Name: y, Length: 1000, dtype: float64, 'treatment': 181    True
422    True
659    True
51     True
684    True
       ...
874    True
146    True
162    True
972    True
426    True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9047
INFO:causalml:    RMSE (Treatment):     0.9695
INFO:causalml:   sMAPE   (Control):     0.4927
INFO:causalml:   sMAPE (Treatment):     0.1624
INFO:causalml:    Gini   (Control):     0.7019
INFO:causalml:    Gini (Treatment):     0.9864
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
716 -1.615944  1.592631 -1.577118 -0.974178  1.417172 -0.195000  0.312512
774 -0.886076  0.195586  0.455056 -0.785569  0.251063 -1.413741  0.262704
272 -3.301648  0.553178  0.159603  2.046934 -0.388566 -1.527966  1.482174
443 -2.529796  1.084026 -0.819423  0.035893  0.753495  0.483814 -0.069741
854 -2.523311 -0.010257 -0.769967 -0.177994  0.185291 -0.178115  0.296577
..        ...       ...       ...       ...       ...       ...       ...
614 -2.386008  1.131412 -0.505893  0.004830  0.101923 -0.350414 -0.390763
826 -1.751728  1.126503 -0.508416  0.008477 -0.550763 -2.243036  0.627975
279 -2.061036 -0.503615 -0.241566 -0.116680  0.053960  0.623388  1.384687
24  -0.551295  0.322400 -1.152389  1.649997 -2.529414 -1.033174  0.650334
312 -0.728990 -0.366328  0.830242  0.664962 -0.175745 -0.044821  0.803275

           X0        X1
716  0.312512 -0.195000
774  0.262704 -1.413741
272  1.482174 -1.527966
443 -0.069741  0.483814
854  0.296577 -0.178115
..        ...       ...
614 -0.390763 -0.350414
826  0.627975 -2.243036
279  1.384687  0.623388
24   0.650334 -1.033174
312  0.803275 -0.044821

[1000 rows x 9 columns], 'y': 716     9.358799
774     6.080336
272    -7.247960
443     6.116446
854    -8.250436
         ...
614    -5.992837
826     3.753175
279    -6.155145
24     -7.244556
312    10.376441
Name: y, Length: 1000, dtype: float64, 'treatment': 716     True
774     True
272    False
443     True
854    False
       ...
614    False
826     True
279    False
24     False
312     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
148 -0.427906 -0.348349 -1.414734 -0.840811 -1.436732 -1.544825  1.050592
683 -0.565221 -1.461757 -1.292868  0.617291  0.720147  0.269804  0.479413
782 -0.683582  2.220805 -0.634412  1.335696 -1.692552 -0.549927  2.153804
323 -1.189912  1.017885 -1.046517 -0.238839 -0.588009  0.098981  0.492976
229 -1.566769  1.166166  0.379113  1.564834  0.393456  0.321811  3.489682
..        ...       ...       ...       ...       ...       ...       ...
303 -1.802541  1.155115  0.033706  1.224001  0.978380 -0.626800  0.580053
356 -1.116602  0.982596 -0.581222 -0.520757 -0.778744  0.510570  1.240015
180 -0.755650  0.531365 -1.277980 -1.074982  1.894625 -0.207842  0.434392
117 -2.281886  0.960677 -2.020716 -1.229467 -1.118221 -0.754915  0.968480
216 -1.445130  1.697550 -2.520408 -0.942383 -0.658474 -1.404127  0.107552

           X0        X1
148  1.050592 -1.544825
683  0.479413  0.269804
782  2.153804 -0.549927
323  0.492976  0.098981
229  3.489682  0.321811
..        ...       ...
303  0.580053 -0.626800
356  1.240015  0.510570
180  0.434392 -0.207842
117  0.968480 -0.754915
216  0.107552 -1.404127

[1000 rows x 9 columns], 'y': 148     4.946877
683    -0.955793
782    14.584633
323     6.435152
229    21.267379
         ...
303    11.687300
356    -4.713297
180    12.418586
117   -11.180244
216     2.376140
Name: y, Length: 1000, dtype: float64, 'treatment': 148     True
683    False
782     True
323     True
229     True
       ...
303     True
356    False
180     True
117    False
216     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0486
INFO:causalml:    RMSE (Treatment):     0.9171
INFO:causalml:   sMAPE   (Control):     0.5529
INFO:causalml:   sMAPE (Treatment):     0.1573
INFO:causalml:    Gini   (Control):     0.7084
INFO:causalml:    Gini (Treatment):     0.9884
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0082
INFO:causalml:    RMSE (Treatment):     0.9557
INFO:causalml:   sMAPE   (Control):     0.5230
INFO:causalml:   sMAPE (Treatment):     0.1729
INFO:causalml:    Gini   (Control):     0.7377
INFO:causalml:    Gini (Treatment):     0.9884

{'X':            W4        W2        W1        W3        W0        X1        X0  \
168  0.450300  1.390503  0.155373 -1.101036 -0.952799 -0.887511  1.430561
176 -0.856629  1.249011  0.865558  0.345553 -2.692244 -0.310500 -0.444710
712 -0.384708  1.043094 -0.342221  1.242046 -0.192181 -0.562779 -1.078074
540 -1.489472  1.850874 -0.563384  0.019167  2.261024  0.812529  1.334890
243 -3.249216 -0.470659  0.290909  1.619751 -1.136002 -0.305133  0.820941
..        ...       ...       ...       ...       ...       ...       ...
78  -1.204209  0.126061  0.575761  1.962184 -2.467364 -0.763519 -0.076058
578 -0.204916 -0.336268 -0.512652 -0.557115 -0.290550 -0.190070  0.805354
580  0.010203  1.941732 -0.233593  0.219375  0.073492 -2.284840 -0.308323
967 -1.474196  1.682763 -1.012478  1.377707 -0.535955 -3.259822  1.413777
878 -1.217552  2.664742 -2.231582  1.034746 -0.096639 -1.053233  0.765971

           X0        X1
168  1.430561 -0.887511
176 -0.444710 -0.310500
712 -1.078074 -0.562779
540  1.334890  0.812529
243  0.820941 -0.305133
..        ...       ...
78  -0.076058 -0.763519
578  0.805354 -0.190070
580 -0.308323 -2.284840
967  1.413777 -3.259822
878  0.765971 -1.053233

[1000 rows x 9 columns], 'y': 168    13.539036
176     0.810587
712     6.780320
540    19.517995
243   -10.483545
         ...
78      2.567970
578    -2.946371
580     8.433110
967    -2.657495
878    11.079847
Name: y, Length: 1000, dtype: float64, 'treatment': 168     True
176     True
712     True
540     True
243    False
       ...
78      True
578    False
580     True
967    False
878     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9013
INFO:causalml:    RMSE (Treatment):     0.9553
INFO:causalml:   sMAPE   (Control):     0.5311
INFO:causalml:   sMAPE (Treatment):     0.1842
INFO:causalml:    Gini   (Control):     0.7100
INFO:causalml:    Gini (Treatment):     0.9884

{'X':            W4        W2        W1        W3        W0        X1        X0  \
787  0.073705  2.848665 -1.943361 -1.359323 -0.878067 -0.436985 -0.313540
906 -1.575434  0.723096  0.059694  2.122850  0.024433  0.756446  0.800562
782 -0.746773  2.414147 -0.784415  1.453598 -1.809795 -0.543201  2.299416
357 -0.378320 -0.884596 -0.669495 -0.885519 -0.360368  0.592724 -0.790022
242 -1.792347  1.490790 -2.219958  0.454298  1.183930 -1.147240 -1.586327
..        ...       ...       ...       ...       ...       ...       ...
5    0.099175 -0.058453 -0.246124 -1.474818 -0.040728 -1.233210 -0.794351
557  0.204105 -0.449736  0.150027  1.430447 -1.525944 -1.113027 -0.164975
545 -1.038618  2.006304  0.732717  1.104913  0.596121  0.500374  0.898176
833  0.101179  0.209330  0.769188  1.227717 -0.727485 -1.215284  1.049029
691 -0.929421  1.731717  0.245810  1.374453 -1.257481 -2.478499  1.097686

           X0        X1
787 -0.313540 -0.436985
906  0.800562  0.756446
782  2.299416 -0.543201
357 -0.790022  0.592724
242 -1.586327 -1.147240
..        ...       ...
5   -0.794351 -1.233210
557 -0.164975 -1.113027
545  0.898176  0.500374
833  1.049029 -1.215284
691  1.097686 -2.478499

[1000 rows x 9 columns], 'y': 787     7.397373
906    11.554534
782    14.584633
357     4.629609
242     1.894728
         ...
5       4.741680
557     5.886610
545    15.065277
833    13.445887
691     9.514980
Name: y, Length: 1000, dtype: float64, 'treatment': 787    True
906    True
782    True
357    True
242    True
       ...
5      True
557    True
545    True
833    True
691    True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9654
INFO:causalml:    RMSE (Treatment):     0.9024
INFO:causalml:   sMAPE   (Control):     0.5116
INFO:causalml:   sMAPE (Treatment):     0.1687
INFO:causalml:    Gini   (Control):     0.6677
INFO:causalml:    Gini (Treatment):     0.9897

{'X':            W4        W2        W1        W3        W0        X1        X0  \
896 -2.465205 -0.999605  1.043228  0.318869 -1.465928 -0.503822 -1.540073
924 -1.012879  1.098058 -0.604224 -1.358602 -0.086915  1.405343  1.440895
512 -1.440033  1.671872  1.455479  2.996854 -1.379065 -1.149984  0.578248
426 -0.315671  1.199681 -0.931325 -0.273054  0.861289 -0.838370 -0.409759
896 -2.555420 -0.866699  1.067359  0.461713 -1.476452 -0.242811 -1.659653
..        ...       ...       ...       ...       ...       ...       ...
507 -1.437455 -0.337114 -1.649122  0.872149 -1.304679 -2.133027  0.785621
204 -1.476774  1.668028 -1.614169  0.703887  1.188127  0.282770  2.283149
170 -1.251671  2.617097 -1.391555  0.287190 -0.554072 -0.494777 -0.731169
406 -1.799370  0.523082 -0.214739 -0.356074  0.020431  1.217244  0.629496
96   1.225402  0.765896 -0.047135  1.089486 -1.500370 -1.094204  0.803605

           X0        X1
896 -1.540073 -0.503822
924  1.440895  1.405343
512  0.578248 -1.149984
426 -0.409759 -0.838370
896 -1.659653 -0.242811
..        ...       ...
507  0.785621 -2.133027
204  2.283149  0.282770
170 -0.731169 -0.494777
406  0.629496  1.217244
96   0.803605 -1.094204

[1000 rows x 9 columns], 'y': 896    -7.004058
924    12.759503
512     8.367137
426     8.947595
896    -7.004058
         ...
507    -8.258449
204    18.498160
170    -2.441764
406     9.585905
96     14.140134
Name: y, Length: 1000, dtype: float64, 'treatment': 896     True
924     True
512     True
426     True
896     True
       ...
507    False
204     True
170    False
406     True
96      True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9740
INFO:causalml:    RMSE (Treatment):     0.9701
INFO:causalml:   sMAPE   (Control):     0.5245
INFO:causalml:   sMAPE (Treatment):     0.1888
INFO:causalml:    Gini   (Control):     0.7661
INFO:causalml:    Gini (Treatment):     0.9887

{'X':            W4        W2        W1        W3        W0        X1        X0  \
531 -1.254262 -0.167187 -1.118310  0.614412  1.198764 -0.896836 -0.290468
740 -1.758760  0.145873 -0.995149  1.417127  0.854187 -0.493563  1.956534
484 -2.266697  0.849631 -1.660032 -0.689205  0.906942 -1.756428 -1.829463
978 -0.867775  2.183924  0.075721  0.981680  0.330304 -0.403923  2.232607
381  0.283811  0.106543 -0.055161 -1.052739 -1.024427 -0.208798 -1.355256
..        ...       ...       ...       ...       ...       ...       ...
846 -1.635453 -0.271491  0.831266  0.243038  0.620326  0.957903  1.160719
314 -0.826652 -0.553605 -2.421504  0.298919  0.060331 -2.639562  1.210031
331 -1.054795  1.357207 -1.162656  0.466176  0.407668 -1.780166  0.733555
532 -0.925465  1.279058 -0.889965 -0.707787 -0.241389 -1.093723  0.034877
147 -2.132299  2.078401 -2.857642 -0.986338  0.869850 -0.541032  1.487452

           X0        X1
531 -0.290468 -0.896836
740  1.956534 -0.493563
484 -1.829463 -1.756428
978  2.232607 -0.403923
381 -1.355256 -0.208798
..        ...       ...
846  1.160719  0.957903
314  1.210031 -2.639562
331  0.733555 -1.780166
532  0.034877 -1.093723
147  1.487452 -0.541032

[1000 rows x 9 columns], 'y': 531     7.735845
740    15.715393
484    -3.918315
978    19.702763
381     2.059536
         ...
846    12.705539
314    -3.811119
331    10.231377
532     5.559274
147    10.091404
Name: y, Length: 1000, dtype: float64, 'treatment': 531     True
740     True
484     True
978     True
381     True
       ...
846     True
314    False
331     True
532     True
147     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.2477
INFO:causalml:    RMSE (Treatment):     1.0436
INFO:causalml:   sMAPE   (Control):     0.5956
INFO:causalml:   sMAPE (Treatment):     0.1984
INFO:causalml:    Gini   (Control):     0.6722
INFO:causalml:    Gini (Treatment):     0.9865

{'X':            W4        W2        W1        W3        W0        X1        X0  \
203 -1.114096  0.744064 -0.690975  0.599358 -0.792642 -1.637673 -0.397490
257 -2.323848  3.016880 -1.700118  1.013322 -0.844318  0.618392 -0.839909
538 -1.038158  2.444865  0.094385  1.527320  1.919538 -1.951172  0.524453
990 -0.000793  0.988923 -0.186809  0.210013 -1.229710  0.232132 -0.020338
731 -0.803858  1.991449 -1.228869  0.189832 -0.297466 -0.369540  1.348036
..        ...       ...       ...       ...       ...       ...       ...
315 -0.833272  0.226528  1.212836  1.772068  0.227297 -0.380696 -0.006080
640 -1.671057  1.467272 -0.375628  1.326509 -1.537542 -0.711127  0.021974
878 -0.962893  2.730247 -2.113489  0.862045 -0.149434 -1.135810  1.112920
610 -0.524884  1.540807 -0.035025 -0.035810 -1.185091 -0.813191  0.907323
385  0.165748 -0.396830 -0.189713  0.224653 -1.901237 -1.310045 -0.920344

           X0        X1
203 -0.397490 -1.637673
257 -0.839909  0.618392
538  0.524453 -1.951172
990 -0.020338  0.232132
731  1.348036 -0.369540
..        ...       ...
315 -0.006080 -0.380696
640  0.021974 -0.711127
878  1.112920 -1.135810
610  0.907323 -0.813191
385 -0.920344 -1.310045

[1000 rows x 9 columns], 'y': 203    -4.155357
257     2.568588
538    16.761939
990     8.714440
731    13.912472
         ...
315    11.545213
640     3.839554
878    11.079847
610     9.248446
385    -1.265529
Name: y, Length: 1000, dtype: float64, 'treatment': 203    False
257     True
538     True
990     True
731     True
       ...
315     True
640     True
878     True
610     True
385     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9891
INFO:causalml:    RMSE (Treatment):     0.9589
INFO:causalml:   sMAPE   (Control):     0.5070
INFO:causalml:   sMAPE (Treatment):     0.1700
INFO:causalml:    Gini   (Control):     0.7410
INFO:causalml:    Gini (Treatment):     0.9883

{'X':            W4        W2        W1        W3        W0        X1        X0  \
584 -0.774744  0.506187  0.455158  0.141210  0.990320  1.628572 -0.711597
841 -0.724696 -0.374489 -1.103751  1.129970  0.018946 -1.663767  0.567341
885 -2.180824 -0.176545 -1.411294  0.282502 -1.568631  0.486085  0.855786
288 -2.020792  1.845664  0.351894  0.727023 -1.539883  0.047874  0.409374
207 -1.188113  0.958327  1.154980  0.336073  1.815925 -2.040130  0.659767
..        ...       ...       ...       ...       ...       ...       ...
352 -0.535340 -0.482681  2.379011  0.602865 -0.376757  0.429827 -0.246536
56  -0.256722  0.790392 -1.288484 -0.016408 -0.773910 -0.151304  0.797364
260 -2.400965 -1.351992 -1.401867  0.351487  0.142640 -1.172623 -1.246456
265 -0.624554  2.322689  0.829394 -1.257380 -0.054393 -0.181632 -0.055043
434 -1.176635  1.246362  0.634036 -0.149590 -0.657726 -2.223801 -0.233063

           X0        X1
584 -0.711597  1.628572
841  0.567341 -1.663767
885  0.855786  0.486085
288  0.409374  0.047874
207  0.659767 -2.040130
..        ...       ...
352 -0.246536  0.429827
56   0.797364 -0.151304
260 -1.246456 -1.172623
265 -0.055043 -0.181632
434 -0.233063 -2.223801

[1000 rows x 9 columns], 'y': 584    11.093724
841     9.168319
885   -11.199742
288     5.235864
207    13.068250
         ...
352     8.970110
56     -1.924324
260    -8.117300
265     9.003069
434     1.849930
Name: y, Length: 1000, dtype: float64, 'treatment': 584     True
841     True
885    False
288     True
207     True
       ...
352     True
56     False
260    False
265     True
434     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.8899
INFO:causalml:    RMSE (Treatment):     0.8863
INFO:causalml:   sMAPE   (Control):     0.5197
INFO:causalml:   sMAPE (Treatment):     0.1595
INFO:causalml:    Gini   (Control):     0.7407
INFO:causalml:    Gini (Treatment):     0.9895
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
941 -0.797345  2.665011  0.065694 -0.869696 -0.413973 -0.687528  0.939537
340  0.395646  2.063995 -0.674216 -0.736351  0.536852 -0.722070  1.665084
303 -1.670465  1.107357 -0.022130  1.189194  0.769526 -0.515271  0.865204
365 -0.221781 -0.295120  0.899051  0.889803  0.424290 -1.638042  1.211189
473 -2.054937  3.017625  0.240892  0.295945 -1.512661 -1.911661  0.565662
..        ...       ...       ...       ...       ...       ...       ...
193 -0.518369  1.617026 -1.830014 -0.741120 -0.465983 -1.906328  0.215485
21  -0.773907  1.110886 -0.544079  1.006438 -0.060069 -1.772730 -0.347305
667 -2.226760  0.866911  0.115075  1.460086 -0.182291  0.234942  1.711669
754 -1.061954 -0.022086 -0.504810  1.309105  0.785156 -0.609433  0.176682
110 -1.835672  2.334799 -0.523204  1.565468 -0.553570  0.776834  1.039780

           X0        X1
941  0.939537 -0.687528
340  1.665084 -0.722070
303  0.865204 -0.515271
365  1.211189 -1.638042
473  0.565662 -1.911661
..        ...       ...
193  0.215485 -1.906328
21  -0.347305 -1.772730
667  1.711669  0.234942
754  0.176682 -0.609433
110  1.039780  0.776834

[1000 rows x 9 columns], 'y': 941     9.630585
340    18.529106
303    11.687300
365    15.195618
473     2.964870
         ...
193     5.787386
21      6.406231
667    11.985876
754     9.673575
110    11.934711
Name: y, Length: 1000, dtype: float64, 'treatment': 941    True
340    True
303    True
365    True
473    True
       ...
193    True
21     True
667    True
754    True
110    True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9757
INFO:causalml:    RMSE (Treatment):     0.9511
INFO:causalml:   sMAPE   (Control):     0.5335
INFO:causalml:   sMAPE (Treatment):     0.1826
INFO:causalml:    Gini   (Control):     0.7234
INFO:causalml:    Gini (Treatment):     0.9876
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator

{'X':            W4        W2        W1        W3        W0        X1        X0  \
439 -0.777678  0.791216  0.088420 -0.890809 -2.096758 -2.250654 -0.541533
448 -1.966394  0.687084 -1.596052  0.310020  1.635333 -0.303399 -0.488773
737 -0.437622  0.229345 -0.010522  0.433842 -1.561250 -1.020391  0.680363
458 -1.283245 -0.238230 -0.732136  1.453553 -1.122988  0.246707  0.593596
507 -1.573522 -0.282174 -1.553324  1.126635 -1.519532 -1.805382  1.103591
..        ...       ...       ...       ...       ...       ...       ...
689 -2.157866  1.144691 -1.437880 -2.358650 -1.930470 -2.442867 -0.507167
817  0.535492  1.129122 -0.785106  0.005089  0.605181 -0.155125  1.160599
979 -1.119402 -0.292695  0.065637  1.221340  1.164068 -0.975426  0.551811
447 -0.452422  0.225148 -0.548859 -0.857193 -0.796498 -1.718787  0.004224
318 -0.436870 -0.478021 -0.215719 -0.049107  1.442010 -0.572800 -0.385856

           X0        X1
439 -0.541533 -2.250654
448 -0.488773 -0.303399
737  0.680363 -1.020391
458  0.593596  0.246707
507  1.103591 -1.805382
..        ...       ...
689 -0.507167 -2.442867
817  1.160599 -0.155125
979  0.551811 -0.975426
447  0.004224 -1.718787
318 -0.385856 -0.572800

[1000 rows x 9 columns], 'y': 439    -2.654670
448    -0.866495
737     6.679250
458     7.125670
507    -8.258449
         ...
689   -13.994008
817    17.203287
979    10.393456
447     3.661764
318    10.068543
Name: y, Length: 1000, dtype: float64, 'treatment': 439     True
448    False
737     True
458     True
507    False
       ...
689    False
817     True
979     True
447     True
318     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1156
INFO:causalml:    RMSE (Treatment):     1.0473
INFO:causalml:   sMAPE   (Control):     0.5300
INFO:causalml:   sMAPE (Treatment):     0.1864
INFO:causalml:    Gini   (Control):     0.7404
INFO:causalml:    Gini (Treatment):     0.9859

{'X':            W4        W2        W1        W3        W0        X1        X0  \
517 -1.249075  0.028192 -0.076209  1.228302 -0.889615 -0.970315  1.053813
1   -0.598908  0.552677 -0.987892  1.753865 -1.047326  0.827995  0.683017
401 -1.902249  1.609008 -1.650454  1.139514 -0.243655  0.337020  0.009184
305 -2.290212  2.079516  1.762446 -1.346023 -0.321697 -0.296516  1.240990
219 -0.424078 -0.746495 -0.269375  1.026374  0.569689 -2.044241  0.193521
..        ...       ...       ...       ...       ...       ...       ...
367 -1.119150  0.790038 -2.083099  0.272993 -2.106983  0.511889 -0.991478
124 -0.979415  2.388193  0.461914  0.399532  0.582675 -0.175566 -0.247623
857 -0.958933  0.848727 -0.274051 -0.994394 -0.419030 -0.062599 -0.356535
946 -0.035807  1.000916  0.683003  1.705651 -0.522502 -1.759322  1.789445
421 -2.133503  0.248461 -0.560940 -0.426672 -0.211597  0.784156 -1.712237

           X0        X1
517  1.053813 -0.970315
1    0.683017  0.827995
401  0.009184  0.337020
305  1.240990 -0.296516
219  0.193521 -2.044241
..        ...       ...
367 -0.991478  0.511889
124 -0.247623 -0.175566
857 -0.356535 -0.062599
946  1.789445 -1.759322
421 -1.712237  0.784156

[1000 rows x 9 columns], 'y': 517    -4.981074
1      11.125450
401     7.798611
305     7.299002
219     8.839168
         ...
367    -8.836089
124    10.147994
857     4.361211
946    16.526801
421    -2.608507
Name: y, Length: 1000, dtype: float64, 'treatment': 517    False
1       True
401     True
305     True
219     True
       ...
367    False
124     True
857     True
946     True
421     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0421
INFO:causalml:    RMSE (Treatment):     1.0731
INFO:causalml:   sMAPE   (Control):     0.5423
INFO:causalml:   sMAPE (Treatment):     0.1840
INFO:causalml:    Gini   (Control):     0.7539

{'X':            W4        W2        W1        W3        W0        X1        X0  \
724 -1.186624 -0.204969  0.236915  0.421616 -0.619602  0.457596  0.993188
386  0.351171  2.087307  2.341325  0.002534 -1.115900  1.480497 -0.390224
53  -2.973900  0.733783 -0.604958  0.645836 -0.335077 -1.900429  2.401266
298 -1.414364  1.106476  0.190108 -2.115642 -1.068247  0.043662  0.829619
365 -0.035523 -0.341759  0.678484  0.982625  0.282776 -1.789647  1.250139
..        ...       ...       ...       ...       ...       ...       ...
217 -1.002155  0.954097  0.201586  0.415424 -1.836686 -1.940836 -0.566890
480 -0.434488  0.173672  0.399111  1.491822  0.365539  0.256138  0.889867
39  -0.999988  1.230452 -1.692300 -0.961869 -1.179618  0.079984  1.729389
327 -1.328455  1.163397 -1.333196  1.018846 -2.697315 -1.532378  0.907498
318 -0.506408 -0.658883 -0.359665 -0.030377  1.475936 -0.510247 -0.247610

           X0        X1
724  0.993188  0.457596
386 -0.390224  1.480497
53   2.401266 -1.900429
298  0.829619  0.043662
365  1.250139 -1.789647
..        ...       ...
217 -0.566890 -1.940836
480  0.889867  0.256138
39   1.729389  0.079984
327  0.907498 -1.532378
318 -0.247610 -0.510247

[1000 rows x 9 columns], 'y': 724     7.856564
386    11.776341
53      7.558222
298     5.375935
365    15.195618
         ...
217    -0.259780
480    15.267030
39     -6.109185
327     1.918859
318    10.068543
Name: y, Length: 1000, dtype: float64, 'treatment': 724     True
386     True
53      True
298     True
365     True
       ...
217     True
480     True
39     False
327     True
318     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    Gini (Treatment):     0.9853
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9553
INFO:causalml:    RMSE (Treatment):     0.9546
INFO:causalml:   sMAPE   (Control):     0.5796
INFO:causalml:   sMAPE (Treatment):     0.1537
INFO:causalml:    Gini   (Control):     0.7471
INFO:causalml:    Gini (Treatment):     0.9884

{'X':            W4        W2        W1        W3        W0        X1        X0  \
207 -1.366234  1.181501  1.211633  0.365055  1.568180 -2.136057  0.943552
469 -1.830332  0.130032  0.392897 -1.851040 -0.910172 -1.795773  2.143216
210 -0.290128  0.926269  1.449719  1.449915  0.314409 -1.436922 -1.237186
477 -2.716049  0.919646 -0.058762  1.140785 -1.477922 -1.039523  1.901640
587 -0.913343 -0.338004  0.824561  1.785646 -0.630247 -0.794141  1.563777
..        ...       ...       ...       ...       ...       ...       ...
358 -1.690018  0.297790 -0.583945  1.387763 -1.053933 -0.484870  0.193280
998 -0.132933  0.842204 -1.539580 -0.012722 -1.320756  0.959635  1.889926
262 -1.701307 -1.234552 -0.291712  1.306074  0.636205 -0.594167  0.592119
846 -1.812872 -0.339279  0.842863  0.273237  0.789526  1.046829  1.325270
295 -0.991002  1.251465 -1.432965  0.507186 -1.377766  0.438091  1.324539

           X0        X1
207  0.943552 -2.136057
469  2.143216 -1.795773
210 -1.237186 -1.436922
477  1.901640 -1.039523
587  1.563777 -0.794141
..        ...       ...
358  0.193280 -0.484870
998  1.889926  0.959635
262  0.592119 -0.594167
846  1.325270  1.046829
295  1.324539  0.438091

[1000 rows x 9 columns], 'y': 207    13.068250
469     4.435916
210     7.048680
477     7.037455
587    12.208485
         ...
358     3.577322
998    13.321122
262     7.461448
846    12.705539
295     9.933260
Name: y, Length: 1000, dtype: float64, 'treatment': 207    True
469    True
210    True
477    True
587    True
       ...
358    True
998    True
262    True
846    True
295    True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1203
INFO:causalml:    RMSE (Treatment):     0.9173
INFO:causalml:   sMAPE   (Control):     0.5700
INFO:causalml:   sMAPE (Treatment):     0.1799
INFO:causalml:    Gini   (Control):     0.7873
INFO:causalml:    Gini (Treatment):     0.9906
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0168
INFO:causalml:    RMSE (Treatment):     0.9727
INFO:causalml:   sMAPE   (Control):     0.6179
INFO:causalml:   sMAPE (Treatment):     0.1711
INFO:causalml:    Gini   (Control):     0.7324
INFO:causalml:    Gini (Treatment):     0.9886
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
969 -1.335965  0.447648 -0.411083  1.979017 -0.138700  2.367625  1.304766
120  0.878535  1.143899 -0.483360  1.389960 -1.129051 -0.036144  0.322280
398 -0.544874  1.460350 -1.210807  0.246759 -1.896953 -1.364403  0.295212
962  0.011323  0.505232 -0.851645 -0.691518 -0.815307 -2.475926 -0.811474
969 -1.471246  0.725484 -0.285822  1.968396  0.098097  2.035384  1.534909
..        ...       ...       ...       ...       ...       ...       ...
156 -2.062858  1.654820 -0.039749  0.633792 -1.023244 -0.324959  1.014639
996 -2.996934  1.835326 -0.415804  1.731529  1.114039  1.000899  1.966124
283 -2.445636  0.615026 -0.362207 -0.613346  1.228393 -1.540628  0.829009
15  -2.063208  1.203270  0.445253  0.477875 -2.216014 -0.031189  0.820581
590 -1.171838  0.232455  0.198807  1.170847  0.340350  0.982074  1.019249

           X0        X1
969  1.304766  2.367625
120  0.322280 -0.036144
398  0.295212 -1.364403
962 -0.811474 -2.475926
969  1.534909  2.035384
..        ...       ...
156  1.014639 -0.324959
996  1.966124  1.000899
283  0.829009 -1.540628
15   0.820581 -0.031189
590  1.019249  0.982074

[1000 rows x 9 columns], 'y': 969    17.059856
120    13.587443
398    -5.105764
962     1.353868
969    17.059856
         ...
156     6.574194
996    17.211685
283     7.081134
15      2.255591
590    13.717742
Name: y, Length: 1000, dtype: float64, 'treatment': 969     True
120     True
398    False
962     True
969     True
       ...
156     True
996     True
283     True
15      True
590     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
762  0.106489  0.709110 -0.977151  0.408870 -0.997734  0.051829  2.205049
561 -0.582679 -0.252048 -0.449171  1.288204 -2.428243  0.213163  1.199164
771 -1.581360  0.727476  0.228220  0.436623  0.611546  1.617179  2.021509
457 -1.325958  1.534327 -0.725483  1.753939 -2.868028 -1.354123  0.004881
665 -2.154642  0.336701 -0.127727 -0.622155  0.322338 -0.912627 -0.105646
..        ...       ...       ...       ...       ...       ...       ...
496 -0.207431  0.517641 -0.880719  1.426367 -0.957096 -0.597891 -0.460067
761 -1.782044 -0.230445 -0.712217 -1.423460  0.959061 -0.572837  1.033045
799 -1.173348  0.516625  1.728205  0.736736 -0.216253  0.221584  3.380923
456 -1.039482  1.682213  0.772552  1.290649  0.140001  0.468885  0.540177
489 -0.254097 -1.425958 -0.688945  0.426641  0.148577 -0.933015 -1.321576

           X0        X1
762  2.205049  0.051829
561  1.199164  0.213163
771  2.021509  1.617179
457  0.004881 -1.354123
665 -0.105646 -0.912627
..        ...       ...
496 -0.460067 -0.597891
761  1.033045 -0.572837
799  3.380923  0.221584
456  0.540177  0.468885
489 -1.321576 -0.933015

[1000 rows x 9 columns], 'y': 762    17.175897
561     8.271140
771    18.593729
457    -8.028104
665     2.331887
         ...
496     5.574781
761     7.959767
799    21.395850
456    12.752919
489     2.992182
Name: y, Length: 1000, dtype: float64, 'treatment': 762     True
561     True
771     True
457    False
665     True
       ...
496     True
761     True
799     True
456     True
489     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0600
INFO:causalml:    RMSE (Treatment):     0.9813
INFO:causalml:   sMAPE   (Control):     0.5765
INFO:causalml:   sMAPE (Treatment):     0.1614
INFO:causalml:    Gini   (Control):     0.7312
INFO:causalml:    Gini (Treatment):     0.9879
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0749

{'X':            W4        W2        W1        W3        W0        X1        X0  \
982 -0.794501  0.663315 -0.444807 -0.869413  0.874432 -1.410284  1.245162
154 -1.522914  2.547789  0.958373 -0.742162  0.153355  0.081442  0.369015
810 -0.413913  0.523270  0.026244  0.698672 -0.373440 -0.178206 -0.843674
130 -0.300918  0.222057 -1.254998  1.360267 -0.882821 -0.874269  0.547756
268  0.108036 -0.440682  0.323611  0.832749 -0.207856 -1.020580 -1.452514
..        ...       ...       ...       ...       ...       ...       ...
934 -2.424483  1.343930 -0.123170  1.154069 -1.879578 -1.717017  1.443784
818 -2.141950  1.077866 -0.176088  0.295325 -2.526841 -0.678777  0.773618
732 -1.838668  1.864397 -0.393074  0.704162 -0.116134 -0.980395  2.071491
646 -0.691894  0.965563  0.295157  1.679703 -1.580214 -0.537919  0.595561
679 -1.193441  1.345853  0.874096 -0.725893 -1.256080  0.695600  0.692764

           X0        X1
982  1.245162 -1.410284
154  0.369015  0.081442
810 -0.843674 -0.178206
130  0.547756 -0.874269
268 -1.452514 -1.020580
..        ...       ...
934  1.443784 -1.717017
818  0.773618 -0.678777
732  2.071491 -0.980395
646  0.595561 -0.537919
679  0.692764  0.695600

[1000 rows x 9 columns], 'y': 982    12.003112
154     9.367612
810     5.956647
130     9.234615
268     3.465358
         ...
934     2.331482
818   -11.770683
732    -3.133831
646     9.399353
679     6.809382
Name: y, Length: 1000, dtype: float64, 'treatment': 982     True
154     True
810     True
130     True
268     True
       ...
934     True
818    False
732    False
646     True
679     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
332 -1.296487  0.315159 -0.917615  1.194753  0.499105  0.293720  1.602796
344 -2.449548  1.486259 -0.160623 -0.389842 -1.081393 -0.050959  1.996267
486 -0.270020 -0.394789 -0.572976  0.377000  0.966363 -1.171396  0.454287
23  -1.579551  0.179087 -0.682078  0.198108  0.152373  0.692088  2.098798
873 -0.179652 -0.287899  0.680093  0.701952 -1.191745 -1.905421  0.095141
..        ...       ...       ...       ...       ...       ...       ...
241 -0.858343  2.347961 -0.871282  1.778801  0.129180 -2.613692 -0.315300
991 -0.297982  0.682839  0.031873 -0.980435 -2.292614 -0.267537  2.225250
737 -0.361140  0.059002 -0.221684  0.200752 -1.620890 -1.393805  1.183499
903 -2.201737  1.581319  0.868458  0.735588  0.787632  1.289393  0.203314
926  1.129385  0.557172 -0.624555  1.080902 -0.720518 -0.244517  0.376367

           X0        X1
332  1.602796  0.293720
344  1.996267 -0.050959
486  0.454287 -1.171396
23   2.098798  0.692088
873  0.095141 -1.905421
..        ...       ...
241 -0.315300 -2.613692
991  2.225250 -0.267537
737  1.183499 -1.393805
903  0.203314  1.289393
926  0.376367 -0.244517

[1000 rows x 9 columns], 'y': 332    16.296656
344    -9.322258
486    12.711964
23     -4.601417
873     4.499265
         ...
241     7.793042
991    -7.648859
737     6.679250
903    11.446682
926    13.032000
Name: y, Length: 1000, dtype: float64, 'treatment': 332     True
344    False
486     True
23     False
873     True
       ...
241     True
991    False
737     True
903     True
926     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    RMSE (Treatment):     1.1122
INFO:causalml:   sMAPE   (Control):     0.5652
INFO:causalml:   sMAPE (Treatment):     0.2127
INFO:causalml:    Gini   (Control):     0.7542
INFO:causalml:    Gini (Treatment):     0.9839
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1086
INFO:causalml:    RMSE (Treatment):     1.0436
INFO:causalml:   sMAPE   (Control):     0.5778
INFO:causalml:   sMAPE (Treatment):     0.1765
INFO:causalml:    Gini   (Control):     0.6881
INFO:causalml:    Gini (Treatment):     0.9868
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0219
INFO:causalml:    RMSE (Treatment):     0.9830
INFO:causalml:   sMAPE   (Control):     0.5468
INFO:causalml:   sMAPE (Treatment):     0.1661
INFO:causalml:    Gini   (Control):     0.7412
INFO:causalml:    Gini (Treatment):     0.9872

{'X':            W4        W2        W1        W3        W0        X1        X0  \
738 -0.484416  1.687269 -1.583798  2.053387 -0.903056 -2.357297  0.762175
403 -1.774697  2.419671  0.581029  0.345242 -2.182253 -0.431024 -1.477473
738 -0.589523  1.673371 -1.640816  2.072849 -0.761020 -2.556119  0.647355
398 -0.430815  1.459252 -1.310444  0.477303 -1.911626 -1.131070  0.406184
540 -1.320015  1.915577 -0.459789 -0.082961  2.080044  0.673359  1.359615
..        ...       ...       ...       ...       ...       ...       ...
185 -0.189739  0.798016 -1.637180 -0.973693  0.515212 -0.479969 -0.733242
903 -2.059504  1.688765  1.129595  0.414466  0.812262  1.673828  0.329361
303 -1.732982  1.160460  0.179380  1.219356  0.864191 -0.416468  0.937511
224 -0.243703  1.430595 -0.902650 -0.183798 -0.514459 -0.550796  1.359030
216 -1.143249  1.699965 -2.537634 -0.834504 -0.963018 -1.343873  0.012237

           X0        X1
738  0.762175 -2.357297
403 -1.477473 -0.431024
738  0.647355 -2.556119
398  0.406184 -1.131070
540  1.359615  0.673359
..        ...       ...
185 -0.733242 -0.479969
903  0.329361  1.673828
303  0.937511 -0.416468
224  1.359030 -0.550796
216  0.012237 -1.343873

[1000 rows x 9 columns], 'y': 738     8.470271
403    -3.265557
738     8.470271
398    -5.105764
540    19.517995
         ...
185    -0.302411
903    11.446682
303    11.687300
224    -0.964343
216     2.376140
Name: y, Length: 1000, dtype: float64, 'treatment': 738     True
403     True
738     True
398    False
540     True
       ...
185    False
903     True
303     True
224    False
216     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
172 -1.043971  1.761026 -1.454093 -0.200212 -0.640173 -1.525210  1.509516
136 -1.924297 -0.274434 -0.917478  1.109700 -0.054534  0.789436 -0.133145
299 -1.192639  0.104373 -0.938420  1.706595  0.154327 -1.158545 -0.509465
13  -1.269414  0.227623  0.692321 -2.352126 -1.858097 -0.389351 -0.227835
876 -0.757699 -0.034899 -0.510357  0.536978 -0.035272 -1.700392  2.046986
..        ...       ...       ...       ...       ...       ...       ...
540 -1.397708  1.894398 -0.492258  0.041512  2.224414  0.784732  1.189514
154 -1.644824  2.600395  0.678450 -0.861557 -0.025366  0.179584  0.538859
678  0.626320  0.739785 -0.184908  0.363249 -1.507676 -0.041604  1.191561
888 -0.079391  0.649108 -1.904946  1.743524  0.441493 -0.063658  1.094585
681 -0.651375  1.471942  0.441373 -1.142466  1.357624 -0.002035  1.670379

           X0        X1
172  1.509516 -1.525210
136 -0.133145  0.789436
299 -0.509465 -1.158545
13  -0.227835 -0.389351
876  2.046986 -1.700392
..        ...       ...
540  1.189514  0.784732
154  0.538859  0.179584
678  1.191561 -0.041604
888  1.094585 -0.063658
681  1.670379 -0.002035

[1000 rows x 9 columns], 'y': 172    10.160763
136    -4.977571
299     5.614664
13     -2.020044
876    12.555557
         ...
540    19.517995
154     9.367612
678    13.061989
888    16.906677
681    18.090454
Name: y, Length: 1000, dtype: float64, 'treatment': 172     True
136    False
299     True
13      True
876     True
       ...
540     True
154     True
678     True
888     True
681     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.2539
INFO:causalml:    RMSE (Treatment):     0.9791
INFO:causalml:   sMAPE   (Control):     0.5269
INFO:causalml:   sMAPE (Treatment):     0.1816
INFO:causalml:    Gini   (Control):     0.7565
INFO:causalml:    Gini (Treatment):     0.9892
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0712
INFO:causalml:    RMSE (Treatment):     0.9911
INFO:causalml:   sMAPE   (Control):     0.5510
INFO:causalml:   sMAPE (Treatment):     0.1923
INFO:causalml:    Gini   (Control):     0.7424
INFO:causalml:    Gini (Treatment):     0.9883

{'X':            W4        W2        W1        W3        W0        X1        X0  \
770 -2.833607  0.247946 -1.054563 -0.451676 -1.223193  0.524625  1.020635
805 -2.511877 -0.416616 -0.952440 -0.180083  0.205770 -2.841067  0.262597
483 -1.372015  0.849386 -0.293284  0.759140 -2.075523  0.228387  0.476496
396 -0.147135 -1.883991 -1.230722 -1.231061 -1.361075  0.330904  0.628008
110 -1.689077  2.282338 -0.370636  1.396179 -0.356844  0.761223  0.814718
..        ...       ...       ...       ...       ...       ...       ...
196 -1.900765  1.142843 -0.900225 -0.632667 -1.317238 -1.090468  0.725665
804 -2.456427  0.834854 -0.079465  1.288538 -1.198145 -0.950941  0.385082
427  0.382613 -0.025500  0.603087  1.308133 -1.791121 -0.510804  2.260320
371 -2.489665  1.949779 -0.742450 -0.413601 -0.449260  0.283632  0.206128
718 -1.214701  1.204254 -2.714157 -0.823141  0.496854 -2.151207  0.308420

           X0        X1
770  1.020635  0.524625
805  0.262597 -2.841067
483  0.476496  0.228387
396  0.628008  0.330904
110  0.814718  0.761223
..        ...       ...
196  0.725665 -1.090468
804  0.385082 -0.950941
427  2.260320 -0.510804
371  0.206128  0.283632
718  0.308420 -2.151207

[1000 rows x 9 columns], 'y': 770   -12.067607
805    -0.721618
483     5.198246
396    -9.105144
110    11.934711
         ...
196     1.676450
804     2.906483
427    15.122153
371     4.468847
718    -3.300467
Name: y, Length: 1000, dtype: float64, 'treatment': 770    False
805     True
483     True
396    False
110     True
       ...
196     True
804     True
427     True
371     True
718    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
563 -0.268443 -0.219708 -2.876535  1.271821 -1.312667  1.280986  0.323887
106 -1.459605  1.569425  0.082599 -0.292869 -1.835696 -2.024142  1.050100
223 -0.733866  0.529961 -0.028252  1.931603 -1.823669 -0.976670  0.713833
27  -0.859815  0.867771 -1.343087 -0.258287 -0.528599 -1.559249  1.140061
960  0.292158 -0.143984 -0.053099 -0.938650 -3.131975 -0.574136  1.483128
..        ...       ...       ...       ...       ...       ...       ...
7    0.169050  1.664113 -1.617550  0.850336 -1.624987 -0.440930  0.929542
961 -1.645919  1.515655 -0.383109  1.373957 -0.598925 -2.405242  0.310820
818 -2.336560  1.184435 -0.133590  0.300783 -2.270370 -0.602481  0.975037
163 -0.801707  0.799981  0.357547  0.710283 -0.733089 -1.185072  0.890325
408 -0.694694  1.651812 -0.792777  0.006787  2.315751 -1.067453 -1.079735

           X0        X1
563  0.323887  1.280986
106  1.050100 -2.024142
223  0.713833 -0.976670
27   1.140061 -1.559249
960  1.483128 -0.574136
..        ...       ...
7    0.929542 -0.440930
961  0.310820 -2.405242
818  0.975037 -0.602481
163  0.890325 -1.185072
408 -1.079735 -1.067453

[1000 rows x 9 columns], 'y': 563    -5.142521
106    -8.631314
223     6.636087
27     -3.423349
960     4.643805
         ...
7      -2.646268
961     4.732956
818   -11.770683
163     8.634512
408    10.095428
Name: y, Length: 1000, dtype: float64, 'treatment': 563    False
106    False
223     True
27     False
960     True
       ...
7      False
961     True
818    False
163     True
408     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.2491
INFO:causalml:    RMSE (Treatment):     1.1114
INFO:causalml:   sMAPE   (Control):     0.5319

{'X':            W4        W2        W1        W3        W0        X1        X0  \
962 -0.009677  0.669116 -0.849799 -0.702391 -0.940493 -2.525755 -0.891971
391 -0.341418  0.951311  1.421517 -1.064450  0.707648  0.191544  0.527653
233  0.198885  1.069432 -1.260303  0.733528  0.155105 -1.662024  2.801471
123 -0.912546  1.504504 -0.369505  1.432430 -2.599077  1.204453 -1.314089
91  -1.832020  1.182525 -0.244803 -0.953757  0.600936 -0.634880  0.917622
..        ...       ...       ...       ...       ...       ...       ...
461 -1.360427 -1.914816  0.510510  0.500077 -0.493602  0.787010  0.798915
957 -0.061057  2.683942 -0.213917  0.549708 -1.910918  0.674554 -0.616028
385  0.122655 -0.360919 -0.231994  0.386600 -1.906796 -1.320416 -1.640399
773 -1.300000 -0.390972 -0.684988 -0.244824 -0.678865 -1.006887 -0.164986
59  -2.485758  0.450724 -0.618499 -0.465156 -1.369240 -1.414008 -1.160067

           X0        X1
962 -0.891971 -2.525755
391  0.527653  0.191544
233  2.801471 -1.662024
123 -1.314089  1.204453
91   0.917622 -0.634880
..        ...       ...
461  0.798915  0.787010
957 -0.616028  0.674554
385 -1.640399 -1.320416
773 -0.164986 -1.006887
59  -1.160067 -1.414008

[1000 rows x 9 columns], 'y': 962     1.353868
391    13.502571
233    21.285516
123     1.168663
91      9.048832
         ...
461    -6.434741
957     6.531621
385    -1.265529
773     1.630931
59    -11.229570
Name: y, Length: 1000, dtype: float64, 'treatment': 962     True
391     True
233     True
123     True
91      True
       ...
461    False
957     True
385     True
773     True
59     False
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:   sMAPE (Treatment):     0.2131
INFO:causalml:    Gini   (Control):     0.7332
INFO:causalml:    Gini (Treatment):     0.9857
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9914
INFO:causalml:    RMSE (Treatment):     1.0505
INFO:causalml:   sMAPE   (Control):     0.5377
INFO:causalml:   sMAPE (Treatment):     0.1699
INFO:causalml:    Gini   (Control):     0.7488
INFO:causalml:    Gini (Treatment):     0.9845
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
404  0.304284 -1.498603 -0.480307  1.267006 -0.620078  0.781348 -0.391429
910  0.456531  1.361552 -1.515220  0.848143  1.245427 -0.631175  1.084272
592 -1.825880  1.633655 -1.557530  0.061253 -0.771143  0.575775  1.090394
820 -0.073384  1.163020 -1.955732  1.416701 -2.601253  1.077416 -0.101785
586 -1.843453  1.412223 -0.709723  0.477786 -1.890833  0.265785  2.900704
..        ...       ...       ...       ...       ...       ...       ...
665 -2.203587  0.162848 -0.052461 -0.901767  0.184596 -0.682588  0.078397
308 -1.094821  0.338785  0.692682  0.770982  1.073404 -0.773410  0.837814
624 -2.036389 -0.353131 -0.077377  1.111249 -1.337650  0.085889  0.918850
588 -1.013102 -0.409262  0.759175  0.322997 -1.904446 -1.039943  0.126042
569 -2.779166  0.520614  0.576555  1.902577  1.473554 -1.378417  2.415496

           X0        X1
404 -0.391429  0.781348
910  1.084272 -0.631175
592  1.090394  0.575775
820 -0.101785  1.077416
586  2.900704  0.265785
..        ...       ...
665  0.078397 -0.682588
308  0.837814 -0.773410
624  0.918850  0.085889
588  0.126042 -1.039943
569  2.415496 -1.378417

[1000 rows x 9 columns], 'y': 404    -1.323791
910    20.169101
592     7.749983
820     4.660715
586    -8.767996
         ...
665     2.331887
308    12.604981
624     5.810979
588    -7.223205
569    16.043709
Name: y, Length: 1000, dtype: float64, 'treatment': 404    False
910     True
592     True
820     True
586    False
       ...
665     True
308     True
624     True
588    False
569     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9916
INFO:causalml:    RMSE (Treatment):     1.0002
INFO:causalml:   sMAPE   (Control):     0.5286
INFO:causalml:   sMAPE (Treatment):     0.1753
INFO:causalml:    Gini   (Control):     0.7347
INFO:causalml:    Gini (Treatment):     0.9868
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1868
INFO:causalml:    RMSE (Treatment):     1.0344
INFO:causalml:   sMAPE   (Control):     0.5700
INFO:causalml:   sMAPE (Treatment):     0.1891
INFO:causalml:    Gini   (Control):     0.7466
INFO:causalml:    Gini (Treatment):     0.9874

{'X':            W4        W2        W1        W3        W0        X1        X0  \
234  0.277636 -0.458319 -1.236754  1.580579 -1.049608 -1.092289  0.073106
892 -1.171172  0.806907 -0.680481 -0.663835 -1.156163 -0.919481 -0.952076
671 -1.789504  2.029038  0.366626  0.671937 -1.483687 -0.112850 -0.811123
833  0.263368  0.184965  0.666979  1.310278 -0.849572 -1.298711  1.324894
304 -0.859428  0.727926  1.667976  2.031498  1.015084  0.994497  1.432807
..        ...       ...       ...       ...       ...       ...       ...
564 -0.722416 -0.238189 -0.521413  1.038525 -0.210707  0.310418  2.076362
894 -0.051398  0.530460 -2.565525  0.448468 -0.949153 -0.288395  1.726858
868 -1.754745  1.130944 -0.308641 -0.052623 -0.531785 -0.459450  1.764856
590 -1.432875  0.242315 -0.043110  1.001279  0.420302  1.061977  1.081231
527 -1.840716 -0.321949 -2.442757  0.317798  0.812937  0.094385  0.851838

           X0        X1
234  0.073106 -1.092289
892 -0.952076 -0.919481
671 -0.811123 -0.112850
833  1.324894 -1.298711
304  1.432807  0.994497
..        ...       ...
564  2.076362  0.310418
894  1.726858 -0.288395
868  1.764856 -0.459450
590  1.081231  1.061977
527  0.851838  0.094385

[1000 rows x 9 columns], 'y': 234     7.980017
892    -0.915984
671    -6.150284
833    13.445887
304    22.259686
         ...
564    16.574010
894    -2.776312
868    -6.358042
590    13.717742
527     8.043131
Name: y, Length: 1000, dtype: float64, 'treatment': 234     True
892     True
671    False
833     True
304     True
       ...
564     True
894    False
868    False
590     True
527     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
892 -1.365191  0.809145 -0.651818 -0.533120 -1.174905 -0.754857 -0.953927
478 -1.009846  1.403938 -1.112725  0.483925  0.288028 -2.041947  1.490763
147 -2.033595  2.003972 -2.905970 -0.874470  0.918767 -0.748644  1.628621
22   0.468947  1.163442  0.184800  3.538828 -0.021932  1.220487  2.210038
955 -0.411135  0.547188 -1.517731  1.258115  1.903441 -0.868937  1.334142
..        ...       ...       ...       ...       ...       ...       ...
503 -1.932080  2.001397 -1.569077  0.379106  0.276418 -0.071977  0.991890
486 -0.153314 -0.096854 -0.783739  0.325289  1.050841 -0.886658  0.377008
848 -1.888484  1.311574 -2.408030  0.264099 -0.420793 -1.231687  0.327405
723 -0.168558  1.249420  0.753321  0.597698 -0.693478 -0.348529  0.523999
78  -1.157900  0.135025  0.432163  1.809337 -2.251005 -0.938530 -0.140407

           X0        X1
892 -0.953927 -0.754857
478  1.490763 -2.041947
147  1.628621 -0.748644
22   2.210038  1.220487
955  1.334142 -0.868937
..        ...       ...
503  0.991890 -0.071977
486  0.377008 -0.886658
848  0.327405 -1.231687
723  0.523999 -0.348529
78  -0.140407 -0.938530

[1000 rows x 9 columns], 'y': 892    -0.915984
478    11.134846
147    10.091404
22     25.918390
955    18.503792
         ...
503    -3.163402
486    12.711964
848    -5.747920
723    12.592456
78      2.567970
Name: y, Length: 1000, dtype: float64, 'treatment': 892     True
478     True
147     True
22      True
955     True
       ...
503    False
486     True
848    False
723     True
78      True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1447
INFO:causalml:    RMSE (Treatment):     0.9871
INFO:causalml:   sMAPE   (Control):     0.5226
INFO:causalml:   sMAPE (Treatment):     0.1729
INFO:causalml:    Gini   (Control):     0.6911
INFO:causalml:    Gini (Treatment):     0.9871

{'X':            W4        W2        W1        W3        W0        X1        X0  \
192  0.013481  0.077942 -0.358307  0.168147 -0.467291 -0.693907  0.667007
417 -0.366190  0.259755 -1.225215 -0.106444 -0.371682 -1.259310  1.718957
799 -1.223681  0.573615  1.814878  0.681714 -0.181330  0.214086  3.431480
286 -0.799104  1.123722  0.147207  0.723531 -1.327644  0.238024  0.305314
498  0.729111  1.362158  0.281377  0.617431 -0.087265  0.119880  0.608881
..        ...       ...       ...       ...       ...       ...       ...
117 -2.231882  0.665366 -1.909693 -1.065803 -1.313906 -0.629881  0.962470
792 -0.774205  0.254451 -0.106932 -1.628313 -0.065050 -0.218405  1.323578
253 -0.250339  0.511999 -0.172158  1.445175 -1.914054  0.621038  0.901125
291  0.333775 -0.137968 -0.339481  0.150972 -0.486684  0.052764  2.652309
463 -1.375221  1.114778 -1.084832  0.642387  0.909748  0.993138  0.657208

           X0        X1
192  0.667007 -0.693907
417  1.718957 -1.259310
799  3.431480  0.214086
286  0.305314  0.238024
498  0.608881  0.119880
..        ...       ...
117  0.962470 -0.629881
792  1.323578 -0.218405
253  0.901125  0.621038
291  2.652309  0.052764
463  0.657208  0.993138

[1000 rows x 9 columns], 'y': 192    10.887872
417    12.283023
799    21.395850
286     7.882269
498    17.581110
         ...
117   -11.180244
792     9.683267
253    11.727481
291    18.348778
463    13.719677
Name: y, Length: 1000, dtype: float64, 'treatment': 192     True
417     True
799     True
286     True
498     True
       ...
117    False
792     True
253     True
291     True
463     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9856
INFO:causalml:    RMSE (Treatment):     0.9593
INFO:causalml:   sMAPE   (Control):     0.5396
INFO:causalml:   sMAPE (Treatment):     0.1518
INFO:causalml:    Gini   (Control):     0.7416
INFO:causalml:    Gini (Treatment):     0.9867
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1492
INFO:causalml:    RMSE (Treatment):     0.9369
INFO:causalml:   sMAPE   (Control):     0.5904

{'X':            W4        W2        W1        W3        W0        X1        X0  \
813 -2.574793  1.778857 -1.366494  0.361929  0.385444 -1.517934  0.547660
414 -0.709890  0.365473 -3.032579  0.495475 -1.578610 -1.055620  2.481256
395 -1.815771  0.441079 -0.282241  0.386440  1.087325 -1.114349  0.567645
458 -1.367800 -0.075564 -0.649272  1.415704 -1.028008  0.048305  0.511034
204 -1.523569  1.641104 -1.394166  0.432445  1.405679  0.444463  1.984802
..        ...       ...       ...       ...       ...       ...       ...
409 -0.474703  0.299137  1.351608 -0.142418 -0.348057  1.312824  2.057486
846 -1.715347 -0.402672  0.816812  0.259425  0.681951  0.791362  1.367577
662 -0.004893  0.721397 -1.350244  1.319241 -2.629019 -2.145902 -0.150837
508  0.387571  0.618235  1.634774 -1.093117  1.003168  0.543222  1.559748
490 -2.459289  2.497210 -0.739107  0.191121  0.310616  1.609913  1.238706

           X0        X1
813  0.547660 -1.517934
414  2.481256 -1.055620
395  0.567645 -1.114349
458  0.511034  0.048305
204  1.984802  0.444463
..        ...       ...
409  2.057486  1.312824
846  1.367577  0.791362
662 -0.150837 -2.145902
508  1.559748  0.543222
490  1.238706  1.609913

[1000 rows x 9 columns], 'y': 813     5.390940
414    -6.908901
395     8.681410
458     7.125670
204    18.498160
         ...
409    17.513846
846    12.705539
662     1.373230
508    19.084238
490    -3.562790
Name: y, Length: 1000, dtype: float64, 'treatment': 813     True
414    False
395     True
458     True
204     True
       ...
409     True
846     True
662     True
508     True
490    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
491  0.170054  0.365630 -0.436193  0.121649  0.655171 -0.755025 -0.481998
155  0.265491  2.283702  0.416064  1.689635 -0.328769  1.362896  1.004507
644  0.192433 -0.486979 -0.263539  0.925781 -2.334375  0.092650  1.466703
918 -1.677469  1.390816  0.913525  1.070667 -0.519165 -0.050166  1.873531
979 -1.379922 -0.553287  0.201718  1.229420  1.009567 -0.628174  0.494019
..        ...       ...       ...       ...       ...       ...       ...
329 -0.807969  0.144493  0.114449  0.363988 -2.069952  0.209398  2.149941
506 -1.793297 -0.478382 -2.362130  1.851526 -2.501745 -1.469484  0.081321
980 -1.484445  1.470760 -1.486318 -0.797821 -1.396926  0.557199  1.886761
678  0.627820  0.704262 -0.283240  0.380088 -1.533414  0.148136  0.994715
175 -1.276874  0.830585 -0.634198  0.967282  0.198645 -0.826272  2.607876

           X0        X1
491 -0.481998 -0.755025
155  1.004507  1.362896
644  1.466703  0.092650
918  1.873531 -0.050166
979  0.494019 -0.628174
..        ...       ...
329  2.149941  0.209398
506  0.081321 -1.469484
980  1.886761  0.557199
678  0.994715  0.148136
175  2.607876 -0.826272

[1000 rows x 9 columns], 'y': 491     8.770425
155    19.147114
644     9.476615
918    12.845128
979    10.393456
         ...
329    10.777317
506   -11.415137
980     9.526360
678    13.061989
175    17.721185
Name: y, Length: 1000, dtype: float64, 'treatment': 491     True
155     True
644     True
918     True
979     True
       ...
329     True
506    False
980     True
678     True
175     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:   sMAPE (Treatment):     0.1664
INFO:causalml:    Gini   (Control):     0.7113
INFO:causalml:    Gini (Treatment):     0.9892
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0798
INFO:causalml:    RMSE (Treatment):     0.9780
INFO:causalml:   sMAPE   (Control):     0.5958
INFO:causalml:   sMAPE (Treatment):     0.1848
INFO:causalml:    Gini   (Control):     0.6842
INFO:causalml:    Gini (Treatment):     0.9876
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
189  0.410161  0.131611  1.306199 -0.669237 -3.213176 -0.015185  1.042416
137 -1.888267  0.840921 -1.225961  0.008345 -1.717243 -0.430938 -1.004454
578 -0.187148 -0.657366 -0.529002 -0.708784 -0.369343 -0.096796  1.079143
600 -1.989738  1.197631 -0.709695  0.095892 -1.323674 -0.340022 -0.786717
882 -2.141692  0.421597 -1.563731 -0.476695  0.927827 -1.031005  0.219039
..        ...       ...       ...       ...       ...       ...       ...
241 -0.696679  2.374122 -0.824048  1.787343  0.229680 -2.830965 -0.440137
217 -1.125068  0.957498  0.151769  0.436910 -1.737025 -1.745240 -0.371585
580 -0.140849  2.105249 -0.247492  0.146588 -0.125673 -2.231402 -0.241249
321 -2.075528  0.044587  0.680838  0.326194 -0.586312 -0.547235  0.670399
545 -1.029716  2.032547  0.704595  1.014036  0.594667  0.421423  0.768095

           X0        X1
189  1.042416 -0.015185
137 -1.004454 -0.430938
578  1.079143 -0.096796
600 -0.786717 -0.340022
882  0.219039 -1.031005
..        ...       ...
241 -0.440137 -2.830965
217 -0.371585 -1.745240
580 -0.241249 -2.231402
321  0.670399 -0.547235
545  0.768095  0.421423

[1000 rows x 9 columns], 'y': 189     5.702358
137    -9.707122
578    -2.946371
600    -2.038553
882    -4.513230
         ...
241     7.793042
217    -0.259780
580     8.433110
321    -6.946700
545    15.065277
Name: y, Length: 1000, dtype: float64, 'treatment': 189     True
137    False
578    False
600     True
882    False
       ...
241     True
217     True
580     True
321    False
545     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0771
INFO:causalml:    RMSE (Treatment):     0.9774
INFO:causalml:   sMAPE   (Control):     0.5323
INFO:causalml:   sMAPE (Treatment):     0.1741
INFO:causalml:    Gini   (Control):     0.7203
INFO:causalml:    Gini (Treatment):     0.9870
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
143 -1.949957  1.517756  2.204676  1.303316 -0.217841 -1.651990 -0.515243
196 -1.949786  1.115287 -0.853560 -0.572337 -1.419509 -1.086555  0.608502
46  -0.888146  1.092166 -0.582271  0.300130 -1.650112  0.469455  1.900898
9   -0.021802  1.130757  1.080258 -1.120494 -0.771512  0.783945 -0.188939
17  -2.283447  0.963726  0.992729  0.659825 -0.523651 -1.637352  2.110831
..        ...       ...       ...       ...       ...       ...       ...
28   0.284972 -1.725446 -0.542758 -1.319756 -0.151833 -0.471153 -0.210616
273 -0.692795  1.302004 -0.819690  1.466807 -1.801295 -0.348019 -2.020364
162 -1.347888  1.001111 -0.895320  0.433071  1.873425 -2.217724  2.456335
695 -0.281676 -0.435856 -0.023010  0.860228 -0.372742 -1.177590  0.391568
794 -1.175251 -0.572507 -0.484807  0.213518  0.123263 -1.627262  0.296319

           X0        X1
143 -0.515243 -1.651990
196  0.608502 -1.086555
46   1.900898  0.469455
9   -0.188939  0.783945
17   2.110831 -1.637352
..        ...       ...
28  -0.210616 -0.471153
273 -2.020364 -0.348019
162  2.456335 -2.217724
695  0.391568 -1.177590
794  0.296319 -1.627262

[1000 rows x 9 columns], 'y': 143     3.430961
196     1.676450
46     -6.116730
9       8.755592
17     10.402576
         ...
28      4.753158
273    -2.115646
162    17.934588
695     8.766754
794     5.470791
Name: y, Length: 1000, dtype: float64, 'treatment': 143     True
196     True
46     False
9       True
17      True
       ...
28      True
273     True
162     True
695     True
794     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
460 -0.475622  1.378347  0.597975  0.184503 -1.056560 -0.774878  0.997804
270 -0.206312  0.198214 -0.002747  0.136514 -0.920272 -0.790116 -0.029807
787  0.083772  2.897460 -1.819668 -1.691378 -0.779529 -0.571429 -0.318654
363 -1.094109  1.596488 -0.209081 -0.635393 -0.348193 -2.724728  1.738500
394 -1.193673  1.783805 -0.658297  0.524736  0.592888  0.449741  0.668359
..        ...       ...       ...       ...       ...       ...       ...
454 -0.374366  0.308604 -1.075751  0.502187 -1.780666 -0.147872  2.126364
125 -1.513287 -1.100623 -1.587157 -0.180659 -0.209123 -1.245457 -0.609627
441  0.191672  1.544473 -1.272017 -1.240143 -0.223964  0.151050  2.278019
717  0.784248  1.796548  0.804206 -0.405701 -1.511015 -1.543735 -0.785670
503 -2.011356  1.992276 -1.586584  0.628595  0.382689 -0.342653  1.223774

           X0        X1
460  0.997804 -0.774878
270 -0.029807 -0.790116
787 -0.318654 -0.571429
363  1.738500 -2.724728
394  0.668359  0.449741
..        ...       ...
454  2.126364 -0.147872
125 -0.609627 -1.245457
441  2.278019  0.151050
717 -0.785670 -1.543735
503  1.223774 -0.342653

[1000 rows x 9 columns], 'y': 460    11.172108
270     6.749300
787     7.397373
363     8.937908
394    12.331256
         ...
454    12.121477
125    -0.052585
441    18.248395
717     6.273750
503    -3.163402
Name: y, Length: 1000, dtype: float64, 'treatment': 460     True
270     True
787     True
363     True
394     True
       ...
454     True
125     True
441     True
717     True
503    False
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1074
INFO:causalml:    RMSE (Treatment):     1.0417
INFO:causalml:   sMAPE   (Control):     0.5225
INFO:causalml:   sMAPE (Treatment):     0.1999
INFO:causalml:    Gini   (Control):     0.7209
INFO:causalml:    Gini (Treatment):     0.9865
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0059
INFO:causalml:    RMSE (Treatment):     1.0071
INFO:causalml:   sMAPE   (Control):     0.4864
INFO:causalml:   sMAPE (Treatment):     0.1695
INFO:causalml:    Gini   (Control):     0.7898
INFO:causalml:    Gini (Treatment):     0.9875
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9534
INFO:causalml:    RMSE (Treatment):     1.0334
INFO:causalml:   sMAPE   (Control):     0.5827
INFO:causalml:   sMAPE (Treatment):     0.1975
INFO:causalml:    Gini   (Control):     0.7350
INFO:causalml:    Gini (Treatment):     0.9860

{'X':            W4        W2        W1        W3        W0        X1        X0  \
525 -1.257118  0.330664 -0.915888  0.671743  0.367080 -0.748137  0.450687
240 -1.118451  0.973362  0.110315  1.164763 -0.021371 -0.742111 -0.164940
35  -0.435681 -0.794540 -0.418219 -0.689760  0.350847 -1.512905 -0.001673
466  0.020113  1.393934 -0.355963  1.711858 -0.590929  0.222977  1.567094
243 -2.996842 -0.425847  0.431792  1.635614 -1.242695 -0.323142  1.143186
..        ...       ...       ...       ...       ...       ...       ...
795 -1.103395  1.605360 -1.577250 -0.179236  0.174861  0.283471 -0.625883
979 -1.343553 -0.429367 -0.073222  1.017619  0.843141 -0.743642  0.564612
446  0.592343  0.313503 -0.041499  1.056622  0.324638  0.685480 -2.317766
663  0.921055  0.125452  0.450490 -0.032753 -0.320248  0.770046  0.389654
245 -1.757050  1.779740 -0.756241 -0.346250 -1.023875 -0.470867  0.000910

           X0        X1
525  0.450687 -0.748137
240 -0.164940 -0.742111
35  -0.001673 -1.512905
466  1.567094  0.222977
243  1.143186 -0.323142
..        ...       ...
795 -0.625883  0.283471
979  0.564612 -0.743642
446 -2.317766  0.685480
663  0.389654  0.770046
245  0.000910 -0.470867

[1000 rows x 9 columns], 'y': 525     9.406466
240     6.512254
35     -2.892738
466    18.638368
243   -10.483545
         ...
795     5.028396
979    10.393456
446     6.907692
663    15.384533
245     2.675580
Name: y, Length: 1000, dtype: float64, 'treatment': 525     True
240     True
35     False
466     True
243    False
       ...
795     True
979     True
446     True
663     True
245     True
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
206  0.103730 -1.222034 -0.933344  0.063977  1.399857  0.000319  2.151908
560 -0.462661 -0.232212 -1.504443 -1.182863 -1.370777  0.188339  1.665963
533 -1.862196  2.125772 -0.836537  0.032782 -2.380123 -1.400905 -1.115644
528 -1.197186  2.033324 -0.122422  0.389914 -1.670525  0.477707  0.919813
463 -1.450262  1.052490 -1.091478  0.585210  1.013792  0.704843  0.793687
..        ...       ...       ...       ...       ...       ...       ...
769 -1.792941  0.756132 -0.350476 -0.365354 -0.166349  0.270195  1.808962
280 -0.952118 -0.646225 -1.024826 -0.408950 -0.476089 -1.776295  0.934463
689 -2.206252  1.297100 -1.432744 -2.462582 -1.985402 -2.328632 -0.045690
723  0.089334  1.225408  0.816162  0.782219 -0.602493 -0.468571  0.482690
696  1.374044  1.843108 -0.542039 -0.043563  1.022823 -1.429075  1.809225

           X0        X1
206  2.151908  0.000319
560  1.665963  0.188339
533 -1.115644 -1.400905
528  0.919813  0.477707
463  0.793687  0.704843
..        ...       ...
769  1.808962  0.270195
280  0.934463 -1.776295
689 -0.045690 -2.328632
723  0.482690 -0.468571
696  1.809225 -1.429075

[1000 rows x 9 columns], 'y': 206    20.680223
560    -7.215301
533    -6.739186
528     8.156158
463    13.719677
         ...
769    -5.821681
280     5.994137
689   -13.994008
723    12.592456
696    23.158781
Name: y, Length: 1000, dtype: float64, 'treatment': 206     True
560    False
533     True
528     True
463     True
       ...
769    False
280     True
689    False
723     True
696     True
Name: v0, Length: 1000, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0897
INFO:causalml:    RMSE (Treatment):     0.9911
INFO:causalml:   sMAPE   (Control):     0.5511
INFO:causalml:   sMAPE (Treatment):     0.1703
INFO:causalml:    Gini   (Control):     0.7123
INFO:causalml:    Gini (Treatment):     0.9874
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0492

{'X':            W4        W2        W1        W3        W0        X1        X0  \
952 -0.660953  0.410799 -1.691342 -0.427253  0.002600 -1.478529  1.918048
877 -0.329610  1.058964 -0.095169 -1.850964  1.588369  1.510118 -0.178392
895 -0.539152 -0.067219 -0.942383  0.206152  0.166660  0.376239  0.972146
114 -0.246959  0.668501 -1.134474  0.249492 -1.934428 -1.135913 -0.308327
457 -1.390795  1.570080 -0.639978  1.787026 -2.865565 -1.465397 -0.257055
..        ...       ...       ...       ...       ...       ...       ...
308 -1.176133  0.314924  0.418085  0.637007  0.911918 -0.742568  0.841509
349 -1.713807  0.945541 -0.100213  0.359346  0.039680 -0.455719  1.268811
764 -1.799079  2.439488 -0.823690 -0.679766 -0.140499  0.202744  1.818416
873 -0.273349 -0.544929  0.632921  0.675695 -0.970007 -1.999815  0.067102
991 -0.262942  0.695928  0.109928 -1.248148 -2.639630 -0.009511  1.550811

           X0        X1
952  1.918048 -1.478529
877 -0.178392  1.510118
895  0.972146  0.376239
114 -0.308327 -1.135913
457 -0.257055 -1.465397
..        ...       ...
308  0.841509 -0.742568
349  1.268811 -0.455719
764  1.818416  0.202744
873  0.067102 -1.999815
991  1.550811 -0.009511

[1000 rows x 9 columns], 'y': 952    -2.868485
877    12.698322
895    12.243751
114     0.119504
457    -8.028104
         ...
308    12.604981
349    10.315484
764    11.907958
873     4.499265
991    -7.648859
Name: y, Length: 1000, dtype: float64, 'treatment': 952    False
877     True
895     True
114     True
457    False
       ...
308     True
349     True
764     True
873     True
991    False
Name: v0, Length: 1000, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
889 -0.164693 -0.098146  0.862596  0.255460 -0.216842 -1.656429  0.520855
883 -1.149449 -0.077862 -0.130952 -0.114547 -1.504078  1.082394  0.597279
495 -0.887332  0.992530  0.348676  0.724410 -0.731744  1.080723 -0.451440
831 -1.405624 -0.589721 -0.969126  0.575371 -2.125845 -1.547868  1.996688
987 -0.779654  1.379589 -0.474635 -0.208440 -0.895621 -0.919435  0.467998
..        ...       ...       ...       ...       ...       ...       ...
931 -0.593921  0.967747 -0.087519 -0.318961 -0.374575 -1.136714  0.905738
930  0.300960  0.603222 -0.368445 -0.424044  0.242424  1.053366 -0.593627
992 -0.972164  0.159489 -2.098758  0.192947 -0.229343  0.044889 -0.560626
817  0.615579  1.267909 -0.445512 -0.048259  0.461022 -0.247928  1.070838
647 -0.053906  1.500408 -0.769493  1.074275 -0.442873  1.186568 -0.897844

           X0        X1
889  0.520855 -1.656429
883  0.597279  1.082394
495 -0.451440  1.080723
831  1.996688 -1.547868
987  0.467998 -0.919435
..        ...       ...
931  0.905738 -1.136714
930 -0.593627  1.053366
992 -0.560626  0.044889
817  1.070838 -0.247928
647 -0.897844  1.186568

[1000 rows x 9 columns], 'y': 889     9.124434
883     5.901248
495     7.428448
831     4.497600
987    -4.527449
         ...
931    10.080089
930    10.114187
992     3.553867
817    17.203287
647     9.825764
Name: y, Length: 1000, dtype: float64, 'treatment': 889     True
883     True
495     True
831     True
987    False
       ...
931     True
930     True
992     True
817     True
647     True
Name: v0, Length: 1000, dtype: bool}

INFO:causalml:    RMSE (Treatment):     1.0259
INFO:causalml:   sMAPE   (Control):     0.4768
INFO:causalml:   sMAPE (Treatment):     0.1909
INFO:causalml:    Gini   (Control):     0.6954
INFO:causalml:    Gini (Treatment):     0.9849
INFO:dowhy.causal_refuters.bootstrap_refuter:Making use of Bootstrap as we have more than 100 examples.
                 Note: The greater the number of examples, the more accurate are the confidence estimates
INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.8 simulated datasets of size 800.0 each
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1266
INFO:causalml:    RMSE (Treatment):     0.7583
INFO:causalml:   sMAPE   (Control):     0.5441
INFO:causalml:   sMAPE (Treatment):     0.1523
INFO:causalml:    Gini   (Control):     0.7189
INFO:causalml:    Gini (Treatment):     0.9944
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0595
INFO:causalml:    RMSE (Treatment):     0.7478
INFO:causalml:   sMAPE   (Control):     0.5545
INFO:causalml:   sMAPE (Treatment):     0.1526
INFO:causalml:    Gini   (Control):     0.7471
INFO:causalml:    Gini (Treatment):     0.9948
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator

{'X':            W4        W2        W1        W3        W0        X1        X0  \
526  1.675724  0.943376  0.067126  0.549606  0.792403 -0.578182  0.606835
938 -1.315956  1.949162 -0.981438 -0.657011  1.021762 -1.702059  0.071287
252 -0.407608 -0.441935 -1.985984  1.444090  0.083796 -0.670731  0.259822
711  0.099864  0.069601 -0.148899  0.008564 -0.642494 -0.050277  0.035692
946 -0.056748  1.082737  0.733627  1.643594 -0.787399 -1.668734  1.968422
..        ...       ...       ...       ...       ...       ...       ...
17  -2.362098  0.982769  0.900772  0.481628 -0.168328 -1.676616  2.100352
789 -1.419550 -0.199775  1.439845 -0.043468 -0.070078  0.347234  1.237806
970 -1.394364  1.786996  1.473136 -0.626023 -0.659760 -1.513833 -0.140064
340  0.375616  2.217136 -0.666164 -0.970808  0.446217 -0.864206  1.763119
150 -2.720297  0.875790 -0.364609 -0.201468  0.001957 -1.103898  0.006235

           X0        X1
526  0.606835 -0.578182
938  0.071287 -1.702059
252  0.259822 -0.670731
711  0.035692 -0.050277
946  1.968422 -1.668734
..        ...       ...
17   2.100352 -1.676616
789  1.237806  0.347234
970 -0.140064 -1.513833
340  1.763119 -0.864206
150  0.006235 -1.103898

[800 rows x 9 columns], 'y': 526    20.291162
938     7.827260
252    -0.786820
711     8.687897
946    16.526801
         ...
17     10.402576
789    11.103887
970     3.837965
340    18.529106
150     1.212438
Name: y, Length: 800, dtype: float64, 'treatment': 526     True
938     True
252    False
711     True
946     True
       ...
17      True
789     True
970     True
340     True
150     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
10   0.846619  2.980613  0.205696  2.969570  0.858587 -1.222764  1.147817
753 -3.041559  0.539260  0.080762 -0.522629 -2.000581 -1.113098  0.679077
832 -0.682884  1.926730 -0.161279 -0.512041 -0.170792 -1.586715  0.243828
332 -1.250673  0.380127 -0.875099  1.332656  0.562342  0.345025  1.790799
396 -0.251196 -1.935299 -1.143646 -1.311957 -1.503472  0.517977  0.501330
..        ...       ...       ...       ...       ...       ...       ...
837 -1.599117  0.022039  1.215839  0.749469 -1.528362 -1.775356  1.715229
568  0.513430  0.522040  0.017360 -0.306262 -0.660055 -1.021097  1.297397
925 -0.292250 -0.487899 -1.791678  1.743438 -1.451384 -1.451548 -0.034151
594 -0.485088  0.198923  0.215890  1.387663  0.491391 -1.501354 -0.527038
270 -0.167769  0.239291 -0.121369  0.249183 -0.697704 -0.932864 -0.077138

           X0        X1
10   1.147817 -1.222764
753  0.679077 -1.113098
832  0.243828 -1.586715
332  1.790799  0.345025
396  0.501330  0.517977
..        ...       ...
837  1.715229 -1.775356
568  1.297397 -1.021097
925 -0.034151 -1.451548
594 -0.527038 -1.501354
270 -0.077138 -0.932864

[800 rows x 9 columns], 'y': 10     24.500212
753    -3.081806
832     7.911675
332    16.296656
396    -9.105144
         ...
837     6.998667
568    13.469854
925     4.053751
594     8.167200
270     6.749300
Name: y, Length: 800, dtype: float64, 'treatment': 10      True
753     True
832     True
332     True
396    False
       ...
837     True
568     True
925     True
594     True
270     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0612
INFO:causalml:    RMSE (Treatment):     0.6918
INFO:causalml:   sMAPE   (Control):     0.5598
INFO:causalml:   sMAPE (Treatment):     0.1391
INFO:causalml:    Gini   (Control):     0.7077
INFO:causalml:    Gini (Treatment):     0.9946
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True

{'X':            W4        W2        W1        W3        W0        X1        X0  \
52   0.100104  1.279976  1.033007  1.442204 -1.120492  0.116882 -0.350810
258 -1.776837  0.805692 -1.001388  0.520858  0.798608 -1.470144  0.534863
631  0.271998  2.338601 -1.112341  1.567964 -0.208038 -0.964256  0.333245
970 -1.394364  1.786996  1.473136 -0.626023 -0.659760 -1.513833 -0.140064
499 -1.577195 -0.178122  0.207028  1.204303 -0.666172  1.145283  0.458618
..        ...       ...       ...       ...       ...       ...       ...
730 -0.464492  1.914988  0.441570  2.087402  0.778967 -0.037934  1.464653
538 -1.000985  2.417859  0.027563  1.436945  1.891137 -2.054885  0.763508
845 -1.215958 -0.485289 -1.167214  0.914403 -1.773236  0.103620 -0.518889
979 -1.320009 -0.547319  0.058828  1.048133  1.078373 -0.857583  0.480911
956 -0.371871 -2.250709  0.444837  1.333937 -1.775519 -1.056909  0.569966

           X0        X1
52  -0.350810  0.116882
258  0.534863 -1.470144
631  0.333245 -0.964256
970 -0.140064 -1.513833
499  0.458618  1.145283
..        ...       ...
730  1.464653 -0.037934
538  0.763508 -2.054885
845 -0.518889  0.103620
979  0.480911 -0.857583
956  0.569966 -1.056909

[800 rows x 9 columns], 'y': 52      9.973921
258     8.016723
631    14.220450
970     3.837965
499     8.058945
         ...
730    20.892188
538    16.761939
845    -8.309502
979    10.393456
956     4.438298
Name: y, Length: 800, dtype: float64, 'treatment': 52      True
258     True
631     True
970     True
499     True
       ...
730     True
538     True
845    False
979     True
956     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
139 -1.494898  1.076908  0.509008 -0.008836  0.810677 -0.868950  0.996169
425 -0.547299  1.010457  0.355019  0.509403 -0.299352 -1.751637  1.623652
512 -1.320082  1.599199  1.561314  2.979497 -1.486751 -1.162078  0.346264
491  0.047326  0.391397 -0.404472  0.060057  0.642744 -0.800913 -0.652640
441  0.096609  1.592914 -1.201277 -1.185385 -0.135004  0.210872  2.310466
..        ...       ...       ...       ...       ...       ...       ...
866 -2.492060  0.007731 -0.940262 -0.496057  1.104068  0.043952  0.520345
838 -0.753922 -0.045816 -0.873461  1.047785 -0.824178 -1.028226 -0.543500
942 -1.242170  1.302580 -0.669569 -0.436052 -0.873213 -1.051785  0.880964
741 -0.491645  2.790552 -1.320505 -0.920479 -1.283653 -0.399134 -0.088422
504 -1.822921  2.092858  0.863527 -0.530852 -1.148241 -0.820791  0.423434

           X0        X1
139  0.996169 -0.868950
425  1.623652 -1.751637
512  0.346264 -1.162078
491 -0.652640 -0.800913
441  2.310466  0.210872
..        ...       ...
866  0.520345  0.043952
838 -0.543500 -1.028226
942  0.880964 -1.051785
741 -0.088422 -0.399134
504  0.423434 -0.820791

[800 rows x 9 columns], 'y': 139    11.778033
425    13.323890
512     8.367137
491     8.770425
441    18.248395
         ...
866     6.414822
838     3.145267
942     6.530430
741     5.593102
504     4.307409
Name: y, Length: 800, dtype: float64, 'treatment': 139    True
425    True
512    True
491    True
441    True
       ...
866    True
838    True
942    True
741    True
504    True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    RMSE   (Control):     3.0428
INFO:causalml:    RMSE (Treatment):     0.7573
INFO:causalml:   sMAPE   (Control):     0.5377
INFO:causalml:   sMAPE (Treatment):     0.1472
INFO:causalml:    Gini   (Control):     0.7402
INFO:causalml:    Gini (Treatment):     0.9938
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0320
INFO:causalml:    RMSE (Treatment):     0.7062
INFO:causalml:   sMAPE   (Control):     0.5521
INFO:causalml:   sMAPE (Treatment):     0.1479
INFO:causalml:    Gini   (Control):     0.7289
INFO:causalml:    Gini (Treatment):     0.9946
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0591
INFO:causalml:    RMSE (Treatment):     0.7414
INFO:causalml:   sMAPE   (Control):     0.5376
INFO:causalml:   sMAPE (Treatment):     0.1489

{'X':            W4        W2        W1        W3        W0        X1        X0  \
556 -1.361029  0.354536 -0.575247 -1.264914 -0.170182 -2.098308 -0.219006
591 -1.762898  1.105254  1.394248  0.278984 -0.431230 -0.144092 -0.668399
603  0.768344  1.258159 -0.927093  0.093637  0.910444 -0.339078  1.272218
65  -2.860295  0.491337  0.307032  0.402665  0.265118 -1.899636 -0.565288
275 -2.120068 -0.318202  0.171676  1.909965 -0.392065 -1.480635 -1.148364
..        ...       ...       ...       ...       ...       ...       ...
238 -0.166010  0.774921 -0.462897 -0.294769 -1.389314 -0.210174  2.431329
377 -2.792060  1.482096 -1.502509 -0.178419 -1.393264 -0.224015 -0.587890
327 -1.263486  1.105278 -1.375127  0.828352 -2.622254 -1.555671  0.808030
721  0.285260 -0.083247 -2.164773  0.446914 -0.582682 -1.433214  0.854157
585  0.931665  0.595916 -0.103318  0.428966  0.099797  0.546834  0.794030

           X0        X1
556 -0.219006 -2.098308
591 -0.668399 -0.144092
603  1.272218 -0.339078
65  -0.565288 -1.899636
275 -1.148364 -1.480635
..        ...       ...
238  2.431329 -0.210174
377 -0.587890 -0.224015
327  0.808030 -1.555671
721  0.854157 -1.433214
585  0.794030  0.546834

[800 rows x 9 columns], 'y': 556     0.746452
591     3.407731
603    19.861939
65     -0.860515
275    -1.163599
         ...
238    14.670040
377    -3.680926
327     1.918859
721     9.936280
585    17.683899
Name: y, Length: 800, dtype: float64, 'treatment': 556    True
591    True
603    True
65     True
275    True
       ...
238    True
377    True
327    True
721    True
585    True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
927 -0.585546  0.718989 -1.390052  1.452628 -1.072161  0.379900  1.646249
501 -1.563067  0.827427  1.267829 -0.259303 -1.929615 -0.584444  0.461660
25  -0.690786  1.699003 -0.408257  0.040733 -2.094357 -0.544787  1.407147
50  -0.250699 -0.035041 -0.605283  0.316777 -1.389142  1.439943  0.671531
858 -2.847642  1.463499  1.836013 -0.281452 -0.828618 -0.584263 -0.020862
..        ...       ...       ...       ...       ...       ...       ...
505 -2.479285  0.847818  0.513598 -0.218587  0.793758 -1.587561  1.340198
478 -1.186330  1.319188 -0.988151  0.448109  0.338470 -1.917601  1.262970
67  -0.786445  0.153501 -0.155530  1.019364 -0.536998 -2.323703 -1.580963
773 -1.096836 -0.432951 -0.837524 -0.231672 -0.535628 -1.020083 -0.353180
971 -1.011609  0.842579 -1.257641  1.094270  0.446977  1.836771  1.109406

           X0        X1
927  1.646249  0.379900
501  0.461660 -0.584444
25   1.407147 -0.544787
50   0.671531  1.439943
858 -0.020862 -0.584263
..        ...       ...
505  1.340198 -1.587561
478  1.262970 -1.917601
67  -1.580963 -2.323703
773 -0.353180 -1.020083
971  1.109406  1.836771

[800 rows x 9 columns], 'y': 927    -2.729883
501     2.593155
25      8.623876
50      9.813211
858     0.976517
         ...
505     8.711479
478    11.134846
67     -0.972001
773     1.630931
971    16.046613
Name: y, Length: 800, dtype: float64, 'treatment': 927    False
501     True
25      True
50      True
858     True
       ...
505     True
478     True
67      True
773     True
971     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    Gini   (Control):     0.7470
INFO:causalml:    Gini (Treatment):     0.9945
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0006
INFO:causalml:    RMSE (Treatment):     0.6701
INFO:causalml:   sMAPE   (Control):     0.5528
INFO:causalml:   sMAPE (Treatment):     0.1413
INFO:causalml:    Gini   (Control):     0.7535
INFO:causalml:    Gini (Treatment):     0.9958
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9126
INFO:causalml:    RMSE (Treatment):     0.7190
INFO:causalml:   sMAPE   (Control):     0.5292
INFO:causalml:   sMAPE (Treatment):     0.1424
INFO:causalml:    Gini   (Control):     0.7453
INFO:causalml:    Gini (Treatment):     0.9944

{'X':            W4        W2        W1        W3        W0        X1        X0  \
189  0.449357  0.147778  1.294543 -0.691706 -3.244261 -0.423599  0.935502
280 -0.924446 -0.495998 -1.067446 -0.475160 -0.560881 -1.593906  1.057462
516 -0.316795  2.424823  0.020510 -0.536910  0.983380 -0.880608  1.127091
343 -1.245795  1.748387  0.828184 -1.775403 -0.972468 -0.866709  0.489782
554 -1.733641 -1.598375  1.607236  0.161280 -1.096390 -2.406156 -0.029761
..        ...       ...       ...       ...       ...       ...       ...
588 -0.930255 -0.378142  0.632255  0.279449 -1.818598 -1.226886  0.208515
517 -1.392397  0.066449 -0.113032  1.352150 -0.975298 -0.875759  1.033629
544 -1.005914  0.255475 -1.627301  1.838873  0.013614 -1.474838  1.050279
622 -0.848690 -0.845794  0.267025 -0.137135  0.356153 -0.001756  0.376872
808 -0.291951  1.043771 -0.571882 -0.537580  0.895934 -1.203433  1.200730

           X0        X1
189  0.935502 -0.423599
280  1.057462 -1.593906
516  1.127091 -0.880608
343  0.489782 -0.866709
554 -0.029761 -2.406156
..        ...       ...
588  0.208515 -1.226886
517  1.033629 -0.875759
544  1.050279 -1.474838
622  0.376872 -0.001756
808  1.200730 -1.203433

[800 rows x 9 columns], 'y': 189     5.702358
280     5.994137
516    16.717229
343     4.731928
554    -8.661899
         ...
588    -7.223205
517    -4.981074
544    10.741956
622     8.845133
808    14.588358
Name: y, Length: 800, dtype: float64, 'treatment': 189     True
280     True
516     True
343     True
554    False
       ...
588    False
517    False
544     True
622     True
808     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
639 -2.186421 -0.027222 -0.566212  0.233679 -0.689588  0.053065  1.482137
95  -0.058838  4.762058  0.823520  0.244461 -0.340888 -2.698332 -0.572900
310 -1.369793  1.308092  0.005217  0.374738 -0.826930 -1.722449 -1.553554
852 -3.069755  1.000289 -1.401000  0.920461  0.886048 -1.676077  0.615709
82  -1.435084  0.702458 -0.750261  1.113334 -0.917545 -0.649801 -1.010522
..        ...       ...       ...       ...       ...       ...       ...
413 -0.344967  1.226283 -2.856753 -0.996308 -3.040920 -0.789789  0.101225
620 -0.173878 -0.822082  0.608737 -0.477850 -0.325762 -0.578554  1.573905
337 -2.268327  0.960651 -1.943224 -0.899046 -0.893776 -0.366596 -0.470081
916 -2.328647  0.329460 -0.298049  0.392304  0.526570  0.266795 -0.277838
741 -0.491645  2.790552 -1.320505 -0.920479 -1.283653 -0.399134 -0.088422

           X0        X1
639  1.482137  0.053065
95  -0.572900 -2.698332
310 -1.553554 -1.722449
852  0.615709 -1.676077
82  -1.010522 -0.649801
..        ...       ...
413  0.101225 -0.789789
620  1.573905 -0.578554
337 -0.470081 -0.366596
916 -0.277838  0.266795
741 -0.088422 -0.399134

[800 rows x 9 columns], 'y': 639     7.178158
95      9.600613
310    -2.023777
852     4.981610
82      0.633027
         ...
413   -10.466301
620    12.522042
337   -10.149884
916     4.529654
741     5.593102
Name: y, Length: 800, dtype: float64, 'treatment': 639     True
95      True
310     True
852     True
82      True
       ...
413    False
620     True
337    False
916     True
741     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9552
INFO:causalml:    RMSE (Treatment):     0.6090
INFO:causalml:   sMAPE   (Control):     0.5162
INFO:causalml:   sMAPE (Treatment):     0.1294
INFO:causalml:    Gini   (Control):     0.7544
INFO:causalml:    Gini (Treatment):     0.9965
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1323
INFO:causalml:    RMSE (Treatment):     0.7235
INFO:causalml:   sMAPE   (Control):     0.5527
INFO:causalml:   sMAPE (Treatment):     0.1525
INFO:causalml:    Gini   (Control):     0.7191
INFO:causalml:    Gini (Treatment):     0.9951

{'X':            W4        W2        W1        W3        W0        X1        X0  \
410 -0.703120  0.450567 -0.504908  0.896033 -1.220663 -3.269095 -1.609582
85  -1.598213  1.517630 -0.586852 -0.114137 -0.935007 -2.850025 -0.268890
207 -1.340535  1.069060  1.086892  0.379268  1.663276 -2.166838  0.824418
286 -0.767221  1.168926  0.081677  0.486259 -1.291802  0.482044  0.276275
895 -0.459769 -0.017087 -0.860174  0.294297  0.061199  0.406711  0.845962
..        ...       ...       ...       ...       ...       ...       ...
705  0.261741  3.188563 -2.010226 -0.517940  1.029141  0.040541  0.750995
529  0.614057 -0.173724  0.014769 -0.498167 -2.710425 -1.591953  0.364769
385  0.098676 -0.311522 -0.271842  0.307693 -1.909032 -1.327209 -1.324019
957 -0.096493  2.758566 -0.130268  0.723499 -1.958197  0.441628 -0.668551
434 -1.319228  1.447448  0.649329 -0.209742 -0.678209 -2.074434 -0.466893

           X0        X1
410 -1.609582 -3.269095
85  -0.268890 -2.850025
207  0.824418 -2.166838
286  0.276275  0.482044
895  0.845962  0.406711
..        ...       ...
705  0.750995  0.040541
529  0.364769 -1.591953
385 -1.324019 -1.327209
957 -0.668551  0.441628
434 -0.466893 -2.074434

[800 rows x 9 columns], 'y': 410    -3.800252
85     -0.384362
207    13.068250
286     7.882269
895    12.243751
         ...
705    18.039614
529     3.288937
385    -1.265529
957     6.531621
434     1.849930
Name: y, Length: 800, dtype: float64, 'treatment': 410    True
85     True
207    True
286    True
895    True
       ...
705    True
529    True
385    True
957    True
434    True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
406 -1.787177  0.601264 -0.241539 -0.288932  0.204519  1.070860  0.778961
98  -0.724707 -0.167508 -2.200046  1.316365 -0.652669  1.877820  0.372580
118 -1.274995  1.158275  0.289024 -0.612569 -0.654518  0.424480  0.256109
581 -0.357955  2.656209 -1.192847 -0.701790 -1.368029 -1.040599  1.376946
792 -0.720148  0.309347 -0.138121 -1.657897 -0.246685 -0.155534  1.208282
..        ...       ...       ...       ...       ...       ...       ...
913 -1.875782  0.226743 -0.219598  2.054710  0.145892 -0.134735  0.897215
141 -0.778642  1.177801 -1.433150  0.422531  1.560049 -1.618745  1.400487
892 -1.218494  0.819447 -0.725373 -0.563789 -1.077169 -0.844040 -0.904803
749 -0.836545  0.213895  1.151435  1.882737 -0.742327 -1.583142  1.220717
32  -0.949214  0.797576  0.148739 -0.683603 -1.245559  0.625313  0.634566

           X0        X1
406  0.778961  1.070860
98   0.372580  1.877820
118  0.256109  0.424480
581  1.376946 -1.040599
792  1.208282 -0.155534
..        ...       ...
913  0.897215 -0.134735
141  1.400487 -1.618745
892 -0.904803 -0.844040
749  1.220717 -1.583142
32   0.634566  0.625313

[800 rows x 9 columns], 'y': 406     9.585905
98     -3.658929
118     6.682644
581    10.512753
792     9.683267
         ...
913    10.638397
141    15.976815
892    -0.915984
749    11.307324
32     -5.941016
Name: y, Length: 800, dtype: float64, 'treatment': 406     True
98     False
118     True
581     True
792     True
       ...
913     True
141     True
892     True
749     True
32     False
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0749
INFO:causalml:    RMSE (Treatment):     0.7530
INFO:causalml:   sMAPE   (Control):     0.5563
INFO:causalml:   sMAPE (Treatment):     0.1509
INFO:causalml:    Gini   (Control):     0.7384
INFO:causalml:    Gini (Treatment):     0.9941
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0160
INFO:causalml:    RMSE (Treatment):     0.7073
INFO:causalml:   sMAPE   (Control):     0.5457
INFO:causalml:   sMAPE (Treatment):     0.1420
INFO:causalml:    Gini   (Control):     0.7585
INFO:causalml:    Gini (Treatment):     0.9950
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator

{'X':            W4        W2        W1        W3        W0        X1        X0  \
909 -0.448326  1.299826  0.401639 -0.500486 -1.006631 -0.906303  1.442992
349 -1.755474  1.024933 -0.194372  0.339537  0.120379 -0.514354  1.189622
814  0.459184  2.326171 -0.114560  0.137966 -0.677452 -1.446683 -0.415932
808 -0.291951  1.043771 -0.571882 -0.537580  0.895934 -1.203433  1.200730
316 -0.460513  0.381744 -1.155697 -0.412682  0.696926 -0.399444  0.467115
..        ...       ...       ...       ...       ...       ...       ...
175 -1.154340  0.806588 -0.662689  0.887440  0.111731 -1.009443  2.862545
586 -1.822525  1.328268 -0.580694  0.434084 -1.937340  0.184012  3.008666
339 -0.174635  1.102320 -0.538718  1.353400 -2.411045 -0.088519 -0.223676
885 -2.040345 -0.486322 -1.615560  0.418978 -1.620201  0.405398  0.986656
775 -2.360643  1.573135 -1.344099  0.823569 -1.137799 -0.227823  0.357754

           X0        X1
909  1.442992 -0.906303
349  1.189622 -0.514354
814 -0.415932 -1.446683
808  1.200730 -1.203433
316  0.467115 -0.399444
..        ...       ...
175  2.862545 -1.009443
586  3.008666  0.184012
339 -0.223676 -0.088519
885  0.986656  0.405398
775  0.357754 -0.227823

[800 rows x 9 columns], 'y': 909    11.235374
349    10.315484
814     8.970856
808    14.588358
316    10.974035
         ...
175    17.721185
586    -8.767996
339     4.815990
885   -11.199742
775    -7.934090
Name: y, Length: 800, dtype: float64, 'treatment': 909     True
349     True
814     True
808     True
316     True
       ...
175     True
586    False
339     True
885    False
775    False
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
680 -2.086015  0.221983  0.305686 -0.239145  0.557483 -2.016304 -0.558481
479 -2.501195  0.440413 -0.336028 -0.086759 -2.148200  0.364346 -0.500635
127 -1.184442  1.633850 -0.489806  0.806896 -0.654125 -0.324203 -1.297638
696  1.393340  1.758799 -0.511376 -0.081397  0.909083 -1.352712  1.852517
391 -0.307159  1.106719  1.338069 -1.242240  0.718523  0.202760  0.500101
..        ...       ...       ...       ...       ...       ...       ...
85  -1.598213  1.517630 -0.586852 -0.114137 -0.935007 -2.850025 -0.268890
944 -1.357226  1.319787  0.307083 -0.175397  0.241389 -2.086970  0.097158
677 -0.926078  0.166307 -2.183101  1.988610 -0.286812 -1.952046  0.131363
71  -0.217006  0.453172 -1.028966  1.065666  0.093415  0.336570  1.457445
877 -0.359565  0.826991 -0.179359 -1.811258  1.418593  1.405163 -0.075609

           X0        X1
680 -0.558481 -2.016304
479 -0.500635  0.364346
127 -1.297638 -0.324203
696  1.852517 -1.352712
391  0.500101  0.202760
..        ...       ...
85  -0.268890 -2.850025
944  0.097158 -2.086970
677  0.131363 -1.952046
71   1.457445  0.336570
877 -0.075609  1.405163

[800 rows x 9 columns], 'y': 680    -4.551836
479    -4.199785
127     2.218793
696    23.158781
391    13.502571
         ...
85     -0.384362
944     5.893961
677     6.044637
71     16.548460
877    12.698322
Name: y, Length: 800, dtype: float64, 'treatment': 680    False
479     True
127     True
696     True
391     True
       ...
85      True
944     True
677     True
71      True
877     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0539
INFO:causalml:    RMSE (Treatment):     0.7237
INFO:causalml:   sMAPE   (Control):     0.5291
INFO:causalml:   sMAPE (Treatment):     0.1414
INFO:causalml:    Gini   (Control):     0.7237
INFO:causalml:    Gini (Treatment):     0.9946
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0446
INFO:causalml:    RMSE (Treatment):     0.7516
INFO:causalml:   sMAPE   (Control):     0.5363
INFO:causalml:   sMAPE (Treatment):     0.1535
INFO:causalml:    Gini   (Control):     0.7290

{'X':            W4        W2        W1        W3        W0        X1        X0  \
703 -0.880997 -1.820627  0.171736 -0.167854  0.599802 -2.011412 -0.082251
839 -0.294596  0.983148 -0.666509  0.796590  0.072181 -0.815520  0.188710
893 -1.491824  0.452183 -1.205184  0.340149  1.739224 -0.632809  0.210046
187  0.166212  1.756207  0.174379  0.739862 -0.309621  0.009743 -0.960418
578 -0.125015 -0.484719 -0.554158 -0.644919 -0.352951 -0.169403  0.807828
..        ...       ...       ...       ...       ...       ...       ...
230 -1.551489  1.387095 -0.588754  0.241508 -1.112741 -0.355862  0.419304
541  0.055686 -0.430488 -0.206977 -1.310188 -0.050773 -0.528455 -0.335622
222 -1.038459  1.255839  0.229480 -2.465858 -0.747697 -0.352680  1.793597
352 -0.314322 -0.439802  2.366604  0.701073 -0.609823  0.603549 -0.256803
8   -1.192098  0.543069  0.448059  1.496816  1.148883 -0.101334  0.352319

           X0        X1
703 -0.082251 -2.011412
839  0.188710 -0.815520
893  0.210046 -0.632809
187 -0.960418  0.009743
578  0.807828 -0.169403
..        ...       ...
230  0.419304 -0.355862
541 -0.335622 -0.528455
222  1.793597 -0.352680
352 -0.256803  0.603549
8    0.352319 -0.101334

[800 rows x 9 columns], 'y': 703    -3.147667
839    10.718157
893    10.444157
187     9.138120
578    -2.946371
         ...
230     5.106041
541     5.970889
222     9.575547
352     8.970110
8      13.374297
Name: y, Length: 800, dtype: float64, 'treatment': 703    False
839     True
893     True
187     True
578    False
       ...
230     True
541     True
222     True
352     True
8       True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
444 -0.480592  0.778385 -2.187205  0.209081 -0.399762 -1.417665  0.987812
436 -0.829198 -0.708516 -0.845920  0.281235  0.360107 -0.575813  0.086546
873 -0.180674 -0.491029  0.704617  0.672175 -1.146367 -2.164384 -0.005079
880 -1.838863  1.807867  2.274113 -0.514016  0.766872 -0.627074  0.533003
336 -3.093855  1.469971  0.367279  1.174817 -0.729766 -2.114001  0.885400
..        ...       ...       ...       ...       ...       ...       ...
377 -2.792060  1.482096 -1.502509 -0.178419 -1.393264 -0.224015 -0.587890
625 -1.336550  0.261883  0.557248 -0.069210  1.161863 -0.761143  1.075224
810 -0.497451  0.532572  0.048320  0.565230 -0.286114 -0.078723 -0.839620
403 -1.630763  2.405041  0.712370  0.529186 -2.230960 -0.416914 -1.593093
229 -1.660012  1.065827  0.301958  1.601869  0.377531  0.371159  3.128694

           X0        X1
444  0.987812 -1.417665
436  0.086546 -0.575813
873 -0.005079 -2.164384
880  0.533003 -0.627074
336  0.885400 -2.114001
..        ...       ...
377 -0.587890 -0.224015
625  1.075224 -0.761143
810 -0.839620 -0.078723
403 -1.593093 -0.416914
229  3.128694  0.371159

[800 rows x 9 columns], 'y': 444     9.305666
436    -2.400060
873     4.499265
880    10.445032
336     2.926169
         ...
377    -3.680926
625    12.619755
810     5.956647
403    -3.265557
229    21.267379
Name: y, Length: 800, dtype: float64, 'treatment': 444     True
436    False
873     True
880     True
336     True
       ...
377     True
625     True
810     True
403     True
229     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    Gini (Treatment):     0.9940
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0310
INFO:causalml:    RMSE (Treatment):     0.6508
INFO:causalml:   sMAPE   (Control):     0.5466
INFO:causalml:   sMAPE (Treatment):     0.1352
INFO:causalml:    Gini   (Control):     0.7524
INFO:causalml:    Gini (Treatment):     0.9958
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0423
INFO:causalml:    RMSE (Treatment):     0.6303
INFO:causalml:   sMAPE   (Control):     0.5129

{'X':            W4        W2        W1        W3        W0        X1        X0  \
749 -0.836545  0.213895  1.151435  1.882737 -0.742327 -1.583142  1.220717
972  0.023127  0.650561 -0.921566  1.750151  1.136720 -1.237667 -0.986899
268 -0.008387 -0.345640  0.297517  0.932659 -0.233741 -0.957095 -1.559348
456 -1.180741  1.678642  0.789419  1.087730  0.037308  0.563234  0.500563
444 -0.480592  0.778385 -2.187205  0.209081 -0.399762 -1.417665  0.987812
..        ...       ...       ...       ...       ...       ...       ...
798  0.298268  1.287738 -0.339625  0.484676 -1.361646 -0.844488  0.584065
119 -0.412019 -1.707939  0.133539  0.898397 -0.839980 -0.812084  1.197569
891 -0.294883 -0.574886 -1.010901  1.587116  0.836384  0.457966  2.071495
649 -2.154564  1.280369 -0.834181  1.662134 -0.490512  0.051132  1.668837
928  0.109927 -0.944171 -0.797629  0.166504 -0.799982  0.651497 -1.165977

           X0        X1
749  1.220717 -1.583142
972 -0.986899 -1.237667
268 -1.559348 -0.957095
456  0.500563  0.563234
444  0.987812 -1.417665
..        ...       ...
798  0.584065 -0.844488
119  1.197569 -0.812084
891  2.071495  0.457966
649  1.668837  0.051132
928 -1.165977  0.651497

[800 rows x 9 columns], 'y': 749    11.307324
972    10.333966
268     3.465358
456    12.752919
444     9.305666
         ...
798    10.206839
119     9.223485
891    20.166272
649    11.515556
928     3.466874
Name: y, Length: 800, dtype: float64, 'treatment': 749    True
972    True
268    True
456    True
444    True
       ...
798    True
119    True
891    True
649    True
928    True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
267 -0.878236  2.441374  0.562945  0.761842 -1.538557 -0.622399  0.837778
261 -1.476037  1.628997 -0.398342  1.161776  0.247181  0.421408 -0.968428
816 -0.262987  0.921280 -0.389069 -0.883456 -2.158824 -1.767588 -0.312548
973  0.403785  0.820598 -1.545243  1.044494  1.061604 -1.578398 -1.347821
172 -1.078399  1.722034 -1.675423 -0.026819 -0.577141 -1.633102  1.619526
..        ...       ...       ...       ...       ...       ...       ...
581 -0.357955  2.656209 -1.192847 -0.701790 -1.368029 -1.040599  1.376946
947 -1.534655 -0.090160 -0.309462 -0.872775 -0.925303 -0.554704  1.455779
349 -1.755474  1.024933 -0.194372  0.339537  0.120379 -0.514354  1.189622
791 -0.302223 -0.035221 -0.779010 -1.067815 -1.088018  1.298864  1.837893
457 -1.376599  1.461089 -0.718660  1.781923 -2.802867 -1.477809  0.128276

           X0        X1
267  0.837778 -0.622399
261 -0.968428  0.421408
816 -0.312548 -1.767588
973 -1.347821 -1.578398
172  1.619526 -1.633102
..        ...       ...
581  1.376946 -1.040599
947  1.455779 -0.554704
349  1.189622 -0.514354
791  1.837893  1.298864
457  0.128276 -1.477809

[800 rows x 9 columns], 'y': 267     9.609806
261     6.314564
816    -0.020121
973     8.500263
172    10.160763
         ...
581    10.512753
947     6.326875
349    10.315484
791    12.700214
457    -8.028104
Name: y, Length: 800, dtype: float64, 'treatment': 267     True
261     True
816     True
973     True
172     True
       ...
581     True
947     True
349     True
791     True
457    False
Name: v0, Length: 800, dtype: bool}

INFO:causalml:   sMAPE (Treatment):     0.1280
INFO:causalml:    Gini   (Control):     0.7234
INFO:causalml:    Gini (Treatment):     0.9959
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9207
INFO:causalml:    RMSE (Treatment):     0.7112
INFO:causalml:   sMAPE   (Control):     0.5407
INFO:causalml:   sMAPE (Treatment):     0.1472
INFO:causalml:    Gini   (Control):     0.7708
INFO:causalml:    Gini (Treatment):     0.9953
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0262
INFO:causalml:    RMSE (Treatment):     0.6868
INFO:causalml:   sMAPE   (Control):     0.5434
INFO:causalml:   sMAPE (Treatment):     0.1467

{'X':            W4        W2        W1        W3        W0        X1        X0  \
418 -1.575082 -0.671851 -0.031660 -0.315301  0.096593  0.101204 -0.833223
113  0.062439  3.227941 -0.979419  0.293534 -0.847729  0.604221 -0.504114
60   0.305279 -0.220252 -0.191311  1.984745 -1.325471  0.975962 -1.100874
135 -1.650996  2.260611 -1.131024  1.404627 -3.453988  0.098573  0.729032
301 -0.802656 -0.970350  0.487567  0.756892 -0.372002 -0.915336  1.021589
..        ...       ...       ...       ...       ...       ...       ...
835  0.331235  0.078864 -0.421835 -0.926551  1.775880  0.278762  1.594422
8   -1.192098  0.543069  0.448059  1.496816  1.148883 -0.101334  0.352319
37  -0.057527  1.104099 -1.337860  0.103293  1.118403 -1.084497  0.679618
49  -0.982952  2.225363 -1.526931 -0.694241  0.029243 -0.455032  0.234971
939 -0.909925  1.045910  0.159272 -0.074724 -0.073039 -1.209146  2.115223

           X0        X1
418 -0.833223  0.101204
113 -0.504114  0.604221
60  -1.100874  0.975962
135  0.729032  0.098573
301  1.021589 -0.915336
..        ...       ...
835  1.594422  0.278762
8    0.352319 -0.101334
37   0.679618 -1.084497
49   0.234971 -0.455032
939  2.115223 -1.209146

[800 rows x 9 columns], 'y': 418    -5.534462
113    10.245959
60      6.681242
135     2.418843
301     9.391349
         ...
835    20.490073
8      13.374297
37     14.525794
49      8.274390
939    14.519592
Name: y, Length: 800, dtype: float64, 'treatment': 418    False
113     True
60      True
135     True
301     True
       ...
835     True
8       True
37      True
49      True
939     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
463 -1.384868  1.213614 -1.227984  0.542554  0.851906  1.016157  0.823336
425 -0.547299  1.010457  0.355019  0.509403 -0.299352 -1.751637  1.623652
615 -1.312910  0.549883 -0.743413  1.273369 -2.435562 -1.071918  2.090585
842  0.197790  0.605362 -0.437344 -0.573307  0.107948 -1.436057  0.995390
762  0.100350  0.689221 -0.950142  0.477153 -0.908423  0.109756  2.284619
..        ...       ...       ...       ...       ...       ...       ...
252 -0.407608 -0.441935 -1.985984  1.444090  0.083796 -0.670731  0.259822
844 -0.961302  1.353622  0.111644  1.015867 -2.161583  0.255054  0.858101
935 -1.612218  0.425603  0.450798  1.795192 -1.030650  0.071948  0.533019
884 -0.166472  2.470375 -0.515409  0.360396 -1.543268 -1.070048  0.645882
596 -1.135855  0.142313  0.627392  0.540314  0.120423 -1.359658  2.273243

           X0        X1
463  0.823336  1.016157
425  1.623652 -1.751637
615  2.090585 -1.071918
842  0.995390 -1.436057
762  2.284619  0.109756
..        ...       ...
252  0.259822 -0.670731
844  0.858101  0.255054
935  0.533019  0.071948
884  0.645882 -1.070048
596  2.273243 -1.359658

[800 rows x 9 columns], 'y': 463    13.719677
425    13.323890
615    -8.516564
842    12.471850
762    17.175897
         ...
252    -0.786820
844    -5.710027
935     7.518694
884     9.392570
596    14.773593
Name: y, Length: 800, dtype: float64, 'treatment': 463     True
425     True
615    False
842     True
762     True
       ...
252    False
844    False
935     True
884     True
596     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    Gini   (Control):     0.7588
INFO:causalml:    Gini (Treatment):     0.9957
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1346
INFO:causalml:    RMSE (Treatment):     0.7431
INFO:causalml:   sMAPE   (Control):     0.5441
INFO:causalml:   sMAPE (Treatment):     0.1588
INFO:causalml:    Gini   (Control):     0.7470
INFO:causalml:    Gini (Treatment):     0.9950
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
575 -1.620041  1.491617 -0.062211  2.124632 -1.193065 -0.186873 -1.131729
619 -0.665633  0.888952 -1.242832 -1.692269  0.683929 -0.545882 -0.557084
323 -1.225390  1.130518 -1.096882 -0.350487 -0.604044  0.001088  0.390490
616 -1.830382  0.390144  1.344299  0.522921 -0.493492 -0.066279 -0.126978
730 -0.464492  1.914988  0.441570  2.087402  0.778967 -0.037934  1.464653
..        ...       ...       ...       ...       ...       ...       ...
538 -1.000985  2.417859  0.027563  1.436945  1.891137 -2.054885  0.763508
56  -0.181805  0.846647 -1.250481 -0.053526 -0.601944 -0.032988  0.712948
135 -1.650996  2.260611 -1.131024  1.404627 -3.453988  0.098573  0.729032
272 -3.198219  0.554460  0.179309  2.032221 -0.401181 -1.528464  1.415503
372 -0.187672  1.029903 -0.511397  0.030915  0.302991  0.331046  2.442863

           X0        X1
575 -1.131729 -0.186873
619 -0.557084 -0.545882
323  0.390490  0.001088
616 -0.126978 -0.066279
730  1.464653 -0.037934
..        ...       ...
538  0.763508 -2.054885
56   0.712948 -0.032988
135  0.729032  0.098573
272  1.415503 -1.528464
372  2.442863  0.331046

[800 rows x 9 columns], 'y': 575     1.988673
619     5.375176
323     6.435152
616     4.604313
730    20.892188
         ...
538    16.761939
56     -1.924324
135     2.418843
272    -7.247960
372    20.420704
Name: y, Length: 800, dtype: float64, 'treatment': 575     True
619     True
323     True
616     True
730     True
       ...
538     True
56     False
135     True
272    False
372     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0101
INFO:causalml:    RMSE (Treatment):     0.7229
INFO:causalml:   sMAPE   (Control):     0.5357
INFO:causalml:   sMAPE (Treatment):     0.1439
INFO:causalml:    Gini   (Control):     0.7439
INFO:causalml:    Gini (Treatment):     0.9947
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0507
INFO:causalml:    RMSE (Treatment):     0.7382
INFO:causalml:   sMAPE   (Control):     0.5534
INFO:causalml:   sMAPE (Treatment):     0.1506
INFO:causalml:    Gini   (Control):     0.7552
INFO:causalml:    Gini (Treatment):     0.9949
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True

{'X':            W4        W2        W1        W3        W0        X1        X0  \
198  0.659512  0.897440  0.294459  0.913949  0.778009  0.324835 -0.038025
134 -2.667587  1.546152  0.275451  1.924880 -1.241239 -1.069396  2.735936
705  0.261741  3.188563 -2.010226 -0.517940  1.029141  0.040541  0.750995
787  0.106762  2.948496 -1.895033 -1.510659 -0.813685 -0.585402 -0.112157
582 -1.856578  1.519025 -0.354817  1.777607 -0.103456 -0.540906  0.894554
..        ...       ...       ...       ...       ...       ...       ...
540 -1.436398  1.926302 -0.447886 -0.040584  2.181020  0.720121  1.462581
720 -0.178107  0.545358 -1.188979 -2.009287 -1.712708  0.183073  2.293672
729 -0.356672  0.996783  0.112431  0.185130 -0.025222 -0.843250 -0.270536
623 -0.801864 -1.215830 -2.351636  1.814226  0.088183 -0.416848  1.256143
500 -0.721612 -0.768412  0.628073  1.455824  1.152325 -0.160033  1.609167

           X0        X1
198 -0.038025  0.324835
134  2.735936 -1.069396
705  0.750995  0.040541
787 -0.112157 -0.585402
582  0.894554 -0.540906
..        ...       ...
540  1.462581  0.720121
720  2.293672  0.183073
729 -0.270536 -0.843250
623  1.256143 -0.416848
500  1.609167 -0.160033

[800 rows x 9 columns], 'y': 198    16.522842
134    11.781552
705    18.039614
787     7.397373
582    10.549091
         ...
540    19.517995
720    11.018665
729     8.272864
623    11.515329
500    17.859054
Name: y, Length: 800, dtype: float64, 'treatment': 198    True
134    True
705    True
787    True
582    True
       ...
540    True
720    True
729    True
623    True
500    True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
359 -1.250892  0.608337 -1.159236  1.196291 -2.448155 -1.418204  2.171817
900 -1.528877  0.854363 -0.020892 -0.125090  1.283825  0.408115 -0.168281
710 -0.753746  2.934628 -1.147550 -0.196935 -0.921997 -0.625230 -0.292340
551 -0.703300  0.665407  1.347444 -0.209059 -0.416380 -0.482778 -0.699907
877 -0.359565  0.826991 -0.179359 -1.811258  1.418593  1.405163 -0.075609
..        ...       ...       ...       ...       ...       ...       ...
330 -1.641813 -0.968627 -0.550355  0.137721  0.993861 -3.243495 -0.204848
41   0.075080 -0.116355 -0.810933  2.222729 -0.150383  0.082462 -0.018712
772 -1.631390  0.465915 -1.149010  1.590925 -0.376748 -1.413846 -0.295266
29  -1.026446  0.591365  0.862837  0.817784 -0.689067 -0.759426 -0.807769
967 -1.386450  1.648145 -0.953457  1.161783 -0.494481 -3.161705  1.259562

           X0        X1
359  2.171817 -1.418204
900 -0.168281  0.408115
710 -0.292340 -0.625230
551 -0.699907 -0.482778
877 -0.075609  1.405163
..        ...       ...
330 -0.204848 -3.243495
41  -0.018712  0.082462
772 -0.295266 -1.413846
29  -0.807769 -0.759426
967  1.259562 -3.161705

[800 rows x 9 columns], 'y': 359     7.556557
900     9.560536
710     5.904376
551     4.944574
877    12.698322
         ...
330    -3.426282
41     12.011428
772     3.250030
29      3.429318
967    -2.657495
Name: y, Length: 800, dtype: float64, 'treatment': 359     True
900     True
710     True
551     True
877     True
       ...
330    False
41      True
772     True
29      True
967    False
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
819 -0.213838  0.707596 -1.514565 -0.247908 -0.337350  0.286133  2.415622
951 -3.474533  0.798959 -1.342694 -0.344446  0.399229 -0.460764  1.828910
138  0.549141  0.618552 -0.411559  1.549844 -1.097545  0.417703 -0.088796
815 -1.745923  1.862168 -1.038948  0.258170 -1.036942 -0.586505 -0.189691
689 -2.243570  1.275778 -1.424485 -2.440678 -1.906424 -2.373430 -0.198568
..        ...       ...       ...       ...       ...       ...       ...
404  0.269959 -1.426995 -0.624877  1.207414 -0.632554  0.826927 -0.342909
28   0.355363 -1.712344 -0.541527 -1.500008 -0.178104 -0.523930 -0.313656
54   1.459268  1.943608  1.129690  0.548664 -1.340973 -0.812905 -0.963531
392  0.391364 -0.989100 -0.665144 -0.463719  1.025926  0.692766 -0.147856
830 -0.930911 -0.544095  0.223413  2.002798 -0.116908 -1.217341  0.878953

           X0        X1
819  2.415622  0.286133
951  1.828910 -0.460764
138 -0.088796  0.417703
815 -0.189691 -0.586505
689 -0.198568 -2.373430
..        ...       ...
404 -0.342909  0.826927
28  -0.313656 -0.523930
54  -0.963531 -0.812905
392 -0.147856  0.692766
830  0.878953 -1.217341

[800 rows x 9 columns], 'y': 819    17.249427
951    -9.429193
138    11.274922
815     2.533898
689   -13.994008
         ...
404    -1.323791
28      4.753158
54      9.737109
392    12.111471
830    -1.062782
Name: y, Length: 800, dtype: float64, 'treatment': 819     True
951    False
138     True
815     True
689    False
       ...
404    False
28      True
54      True
392     True
830    False
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    RMSE   (Control):     3.0887
INFO:causalml:    RMSE (Treatment):     0.7302
INFO:causalml:   sMAPE   (Control):     0.5598
INFO:causalml:   sMAPE (Treatment):     0.1484
INFO:causalml:    Gini   (Control):     0.7513
INFO:causalml:    Gini (Treatment):     0.9947
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0181
INFO:causalml:    RMSE (Treatment):     0.7458
INFO:causalml:   sMAPE   (Control):     0.5518
INFO:causalml:   sMAPE (Treatment):     0.1555
INFO:causalml:    Gini   (Control):     0.7430
INFO:causalml:    Gini (Treatment):     0.9943
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0144
INFO:causalml:    RMSE (Treatment):     0.6481
INFO:causalml:   sMAPE   (Control):     0.5533
INFO:causalml:   sMAPE (Treatment):     0.1329
INFO:causalml:    Gini   (Control):     0.7747

{'X':            W4        W2        W1        W3        W0        X1        X0  \
843 -1.819689  2.321605  1.205835  1.378205 -0.687589 -1.421073 -0.008063
171 -1.142720  1.363980 -1.309646 -0.806699 -1.397706  0.591713 -0.245067
554 -1.733641 -1.598375  1.607236  0.161280 -1.096390 -2.406156 -0.029761
343 -1.245795  1.748387  0.828184 -1.775403 -0.972468 -0.866709  0.489782
19  -0.472917 -0.200341 -0.794933  2.274586 -1.165631 -1.449257  0.217406
..        ...       ...       ...       ...       ...       ...       ...
823  0.865119  0.256833 -0.057368  0.192438  0.700995 -1.560342  1.492618
456 -1.180741  1.678642  0.789419  1.087730  0.037308  0.563234  0.500563
913 -1.875782  0.226743 -0.219598  2.054710  0.145892 -0.134735  0.897215
74  -0.996694  0.160173 -2.637286 -2.023282  1.303478 -1.990375  1.207325
424 -1.778586 -0.041397  0.547042 -0.146728 -0.207960  0.620439  1.840962

           X0        X1
843 -0.008063 -1.421073
171 -0.245067  0.591713
554 -0.029761 -2.406156
343  0.489782 -0.866709
19   0.217406 -1.449257
..        ...       ...
823  1.492618 -1.560342
456  0.500563  0.563234
913  0.897215 -0.134735
74   1.207325 -1.990375
424  1.840962  0.620439

[800 rows x 9 columns], 'y': 843     6.032900
171     2.573635
554    -8.661899
343     4.731928
19      6.700542
         ...
823    18.415087
456    12.752919
913    10.638397
74      8.688916
424    -5.690040
Name: y, Length: 800, dtype: float64, 'treatment': 843     True
171     True
554    False
343     True
19      True
       ...
823     True
456     True
913     True
74      True
424    False
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
943 -2.252890  0.804815 -1.189588  0.399429 -1.924218 -1.450234  0.482038
348 -0.822689  1.950660 -1.773724  0.367062 -1.388543 -1.489743  1.572649
940  1.618120 -0.305935 -1.002070  0.408436 -0.407028 -1.124055  0.103301
406 -1.787177  0.601264 -0.241539 -0.288932  0.204519  1.070860  0.778961
739 -1.946497  0.850165 -0.030473  0.710454  0.237677 -0.545032 -0.878717
..        ...       ...       ...       ...       ...       ...       ...
453 -1.249839 -0.644978 -0.359153  1.084902 -0.516596 -0.365041 -0.139304
583 -0.820218 -0.945137 -1.568877 -0.082810  0.614237 -0.346996  0.919443
963 -1.400546  1.656134 -0.736065  1.479252 -1.067357 -1.225327  0.845018
625 -1.336550  0.261883  0.557248 -0.069210  1.161863 -0.761143  1.075224
375 -1.851590 -0.485257 -0.505663 -0.368564 -1.023961 -1.380998  1.320727

           X0        X1
943  0.482038 -1.450234
348  1.572649 -1.489743
940  0.103301 -1.124055
406  0.778961  1.070860
739 -0.878717 -0.545032
..        ...       ...
453 -0.139304 -0.365041
583  0.919443 -0.346996
963  0.845018 -1.225327
625  1.075224 -0.761143
375  1.320727 -1.380998

[800 rows x 9 columns], 'y': 943   -10.951371
348     9.469476
940    12.297833
406     9.585905
739    -3.295801
         ...
453     4.442757
583    -2.819370
963     7.905661
625    12.619755
375     3.740574
Name: y, Length: 800, dtype: float64, 'treatment': 943    False
348     True
940     True
406     True
739    False
       ...
453     True
583    False
963     True
625     True
375     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    Gini (Treatment):     0.9962
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0228
INFO:causalml:    RMSE (Treatment):     0.6860
INFO:causalml:   sMAPE   (Control):     0.5510
INFO:causalml:   sMAPE (Treatment):     0.1466
INFO:causalml:    Gini   (Control):     0.7470
INFO:causalml:    Gini (Treatment):     0.9953
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9469
INFO:causalml:    RMSE (Treatment):     0.7414
INFO:causalml:   sMAPE   (Control):     0.5231

{'X':            W4        W2        W1        W3        W0        X1        X0  \
364 -1.078590  1.563574  1.504080 -0.486241 -1.540033 -0.435152  0.956055
932 -1.213129  0.643221 -0.007752  0.320722 -1.204954  0.065085 -0.764774
634 -0.085908  0.582399 -1.871995 -0.128561  1.399274 -2.013647 -0.399001
840  0.863115  0.884267  0.385888  1.081802 -0.236509 -0.114485  0.397843
671 -1.805567  1.966280  0.355705  0.675031 -1.531579 -0.217720 -0.701737
..        ...       ...       ...       ...       ...       ...       ...
616 -1.830382  0.390144  1.344299  0.522921 -0.493492 -0.066279 -0.126978
850 -1.559250  1.506724  0.449602  0.956745 -1.983230 -0.410145  2.749812
144 -0.199659  1.050489  1.034230  1.426123  0.412507  0.336879  2.230533
558 -0.550544  0.440434 -0.750181  1.612420  0.551244  0.168593  1.698572
843 -1.819689  2.321605  1.205835  1.378205 -0.687589 -1.421073 -0.008063

           X0        X1
364  0.956055 -0.435152
932 -0.764774  0.065085
634 -0.399001 -2.013647
840  0.397843 -0.114485
671 -0.701737 -0.217720
..        ...       ...
616 -0.126978 -0.066279
850  2.749812 -0.410145
144  2.230533  0.336879
558  1.698572  0.168593
843 -0.008063 -1.421073

[800 rows x 9 columns], 'y': 364     7.693260
932     1.654133
634     8.990907
840    15.748095
671    -6.150284
         ...
616     4.604313
850    12.795708
144    22.522110
558    18.249687
843     6.032900
Name: y, Length: 800, dtype: float64, 'treatment': 364     True
932     True
634     True
840     True
671    False
       ...
616     True
850     True
144     True
558     True
843     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
368 -2.298170  1.146523 -1.218792 -0.454870 -0.475421 -1.530454  0.327366
156 -1.847964  1.488048 -0.079643  0.746560 -1.203496 -0.411978  0.865581
728  0.021921  2.347870  1.993858 -2.023822 -0.508237 -0.879659  0.131258
822 -0.308029  1.239229 -1.712106  2.618474 -0.731033 -0.360836  1.878272
503 -2.066140  2.033010 -1.559405  0.352297  0.418520 -0.333911  1.084480
..        ...       ...       ...       ...       ...       ...       ...
805 -2.622025 -0.308385 -0.881486 -0.183280  0.289018 -2.986494  0.245462
25  -0.690786  1.699003 -0.408257  0.040733 -2.094357 -0.544787  1.407147
402 -2.267259  2.356874 -0.312747 -0.267666 -0.938556  0.873383  1.145311
643 -0.837543  2.690282 -0.956587  1.519004 -3.148478  1.188059  1.128666
962 -0.058529  0.632542 -0.738886 -0.663994 -0.868324 -2.555294 -0.708468

           X0        X1
368  0.327366 -1.530454
156  0.865581 -0.411978
728  0.131258 -0.879659
822  1.878272 -0.360836
503  1.084480 -0.333911
..        ...       ...
805  0.245462 -2.986494
25   1.407147 -0.544787
402  1.145311  0.873383
643  1.128666  1.188059
962 -0.708468 -2.555294

[800 rows x 9 columns], 'y': 368     1.367327
156     6.574194
728     9.374205
822    17.217532
503    -3.163402
         ...
805    -0.721618
25      8.623876
402     8.213659
643     9.118838
962     1.353868
Name: y, Length: 800, dtype: float64, 'treatment': 368     True
156     True
728     True
822     True
503    False
       ...
805     True
25      True
402     True
643     True
962     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:   sMAPE (Treatment):     0.1503
INFO:causalml:    Gini   (Control):     0.7236
INFO:causalml:    Gini (Treatment):     0.9936
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0841
INFO:causalml:    RMSE (Treatment):     0.7612
INFO:causalml:   sMAPE   (Control):     0.5448
INFO:causalml:   sMAPE (Treatment):     0.1577
INFO:causalml:    Gini   (Control):     0.7382
INFO:causalml:    Gini (Treatment):     0.9938
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1236
INFO:causalml:    RMSE (Treatment):     0.7095
INFO:causalml:   sMAPE   (Control):     0.5382
INFO:causalml:   sMAPE (Treatment):     0.1403
INFO:causalml:    Gini   (Control):     0.7091
INFO:causalml:    Gini (Treatment):     0.9948
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True

{'X':            W4        W2        W1        W3        W0        X1        X0  \
647  0.107638  1.405276 -0.825606  1.040288 -0.390118  1.171509 -0.898491
656 -1.273620  1.797036  2.092367  0.953316  1.197489 -0.625639  0.731893
704 -2.163862  1.694504 -1.310167  0.084654 -0.510292 -1.743878 -0.389615
691 -0.985721  1.895540  0.176961  1.219720 -1.233766 -2.238988  1.283650
665 -2.174730  0.248624 -0.092817 -0.715735  0.210519 -0.742166 -0.077510
..        ...       ...       ...       ...       ...       ...       ...
187  0.166212  1.756207  0.174379  0.739862 -0.309621  0.009743 -0.960418
139 -1.494898  1.076908  0.509008 -0.008836  0.810677 -0.868950  0.996169
86  -1.171030  0.999708 -0.474575 -0.002353  1.214732 -0.432952 -1.419492
981 -1.677948 -1.622248 -1.171652  0.349836 -2.213015  1.101661  0.722546
435 -1.214834  1.792454 -0.114462  0.368086 -0.765766  0.747054  1.689047

           X0        X1
647 -0.898491  1.171509
656  0.731893 -0.625639
704 -0.389615 -1.743878
691  1.283650 -2.238988
665 -0.077510 -0.742166
..        ...       ...
187 -0.960418  0.009743
139  0.996169 -0.868950
86  -1.419492 -0.432952
981  0.722546  1.101661
435  1.689047  0.747054

[800 rows x 9 columns], 'y': 647     9.825764
656    15.632059
704     0.037896
691     9.514980
665     2.331887
         ...
187     9.138120
139    11.778033
86      4.893758
981   -12.765797
435    13.857254
Name: y, Length: 800, dtype: float64, 'treatment': 647     True
656     True
704     True
691     True
665     True
       ...
187     True
139     True
86      True
981    False
435     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
914 -0.033438  1.223195  0.288715 -0.542490 -0.948134  0.723902  1.010993
658 -0.901419  0.202237 -2.133776  0.712530 -0.236428 -0.743572  0.753088
366 -1.403735  1.313386  1.988391 -0.578266  1.442075  1.501325  0.150474
775 -2.360643  1.573135 -1.344099  0.823569 -1.137799 -0.227823  0.357754
519 -0.928551  1.643629 -0.012840 -0.089474 -1.289071 -0.902553  0.551816
..        ...       ...       ...       ...       ...       ...       ...
216 -1.185130  1.723057 -2.436984 -0.947938 -0.797647 -1.237892  0.020300
121  1.534365  1.840154 -0.218035 -0.257249 -0.734629 -0.767329 -1.295119
495 -0.937219  0.875867  0.303746  0.659802 -0.598222  1.107266 -0.420269
619 -0.665633  0.888952 -1.242832 -1.692269  0.683929 -0.545882 -0.557084
942 -1.242170  1.302580 -0.669569 -0.436052 -0.873213 -1.051785  0.880964

           X0        X1
914  1.010993  0.723902
658  0.753088 -0.743572
366  0.150474  1.501325
775  0.357754 -0.227823
519  0.551816 -0.902553
..        ...       ...
216  0.020300 -1.237892
121 -1.295119 -0.767329
495 -0.420269  1.107266
619 -0.557084 -0.545882
942  0.880964 -1.051785

[800 rows x 9 columns], 'y': 914    12.801840
658     8.483868
366    13.879918
775    -7.934090
519     6.513308
         ...
216     2.376140
121     8.541463
495     7.428448
619     5.375176
942     6.530430
Name: y, Length: 800, dtype: float64, 'treatment': 914     True
658     True
366     True
775    False
519     True
       ...
216     True
121     True
495     True
619     True
942     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
641 -2.239311  2.194117 -0.353902  0.360330  0.632691 -1.221853 -0.388297
470 -0.119189  2.038863 -0.185731 -0.858254 -2.226518 -0.985301 -1.027339
507 -1.568032 -0.180577 -1.663979  0.966254 -1.411487 -2.027152  0.819706
826 -1.770524  1.148010 -0.657719  0.002402 -0.619052 -2.314279  0.680449
464 -1.458938  1.066085 -0.038658 -0.458790  0.563506  0.649619  0.007999
..        ...       ...       ...       ...       ...       ...       ...
754 -1.008916  0.089333 -0.381037  1.182553  0.653293 -0.475920  0.036262
305 -2.363560  1.924215  1.711203 -1.273183 -0.441145 -0.403834  1.202634
855 -1.525195  2.679573  0.256933 -0.844142 -0.237770 -0.840204  0.824960
248 -0.322096  0.956739 -1.722687  0.445099  1.192956 -0.951015 -0.322611
649 -2.154564  1.280369 -0.834181  1.662134 -0.490512  0.051132  1.668837

           X0        X1
641 -0.388297 -1.221853
470 -1.027339 -0.985301
507  0.819706 -2.027152
826  0.680449 -2.314279
464  0.007999  0.649619
..        ...       ...
754  0.036262 -0.475920
305  1.202634 -0.403834
855  0.824960 -0.840204
248 -0.322611 -0.951015
649  1.668837  0.051132

[800 rows x 9 columns], 'y': 641     4.862547
470    -0.021734
507    -8.258449
826     3.753175
464     8.601975
         ...
754     9.673575
305     7.299002
855     8.939223
248    10.511960
649    11.515556
Name: y, Length: 800, dtype: float64, 'treatment': 641     True
470     True
507    False
826     True
464     True
       ...
754     True
305     True
855     True
248     True
649     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    RMSE   (Control):     3.0128
INFO:causalml:    RMSE (Treatment):     0.7183
INFO:causalml:   sMAPE   (Control):     0.5521
INFO:causalml:   sMAPE (Treatment):     0.1389
INFO:causalml:    Gini   (Control):     0.7298
INFO:causalml:    Gini (Treatment):     0.9944
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1668
INFO:causalml:    RMSE (Treatment):     0.7702
INFO:causalml:   sMAPE   (Control):     0.5480
INFO:causalml:   sMAPE (Treatment):     0.1526
INFO:causalml:    Gini   (Control):     0.7192
INFO:causalml:    Gini (Treatment):     0.9944
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9965
INFO:causalml:    RMSE (Treatment):     0.7273
INFO:causalml:   sMAPE   (Control):     0.5443
INFO:causalml:   sMAPE (Treatment):     0.1442
INFO:causalml:    Gini   (Control):     0.7526
INFO:causalml:    Gini (Treatment):     0.9950

{'X':            W4        W2        W1        W3        W0        X1        X0  \
897 -0.501089 -1.031997 -1.334448 -0.012652 -0.451207 -0.315850 -0.369898
172 -1.078399  1.722034 -1.675423 -0.026819 -0.577141 -1.633102  1.619526
20  -0.810576  1.360309 -0.656243  1.132048 -0.926051  0.651433  1.648609
323 -1.225390  1.130518 -1.096882 -0.350487 -0.604044  0.001088  0.390490
900 -1.528877  0.854363 -0.020892 -0.125090  1.283825  0.408115 -0.168281
..        ...       ...       ...       ...       ...       ...       ...
779 -1.245329  0.830018  0.144683  0.171689  0.322303 -0.800108 -0.118539
556 -1.361029  0.354536 -0.575247 -1.264914 -0.170182 -2.098308 -0.219006
461 -1.364660 -1.842826  0.241333  0.373454 -0.367738  0.860921  0.880397
958 -0.495343  0.847053 -0.028871 -0.718100 -0.871586 -0.630299 -0.132512
49  -0.982952  2.225363 -1.526931 -0.694241  0.029243 -0.455032  0.234971

           X0        X1
897 -0.369898 -0.315850
172  1.619526 -1.633102
20   1.648609  0.651433
323  0.390490  0.001088
900 -0.168281  0.408115
..        ...       ...
779 -0.118539 -0.800108
556 -0.219006 -2.098308
461  0.880397  0.860921
958 -0.132512 -0.630299
49   0.234971 -0.455032

[800 rows x 9 columns], 'y': 897    -4.566144
172    10.160763
20     14.509975
323     6.435152
900     9.560536
         ...
779     7.001850
556     0.746452
461    -6.434741
958     5.051010
49      8.274390
Name: y, Length: 800, dtype: float64, 'treatment': 897    False
172     True
20      True
323     True
900     True
       ...
779     True
556     True
461    False
958     True
49      True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
979 -1.320009 -0.547319  0.058828  1.048133  1.078373 -0.857583  0.480911
786  1.321150  2.395540 -1.313403  1.003881 -1.291792 -0.548760 -1.106383
809 -1.846180  1.505397  0.539237  1.200542 -1.354442 -0.559294  0.909066
941 -0.858300  2.689664  0.060630 -0.800292 -0.649474 -0.542851  0.680252
342 -1.702353 -0.506216 -1.938204 -0.079481 -0.042667 -1.379694  0.242173
..        ...       ...       ...       ...       ...       ...       ...
371 -2.437457  1.813953 -0.635430 -0.294641 -0.408002  0.338137  0.274353
299 -1.370319  0.104371 -0.773003  1.741095  0.119556 -1.227923 -0.272634
234  0.299190 -0.331887 -1.279492  1.419962 -1.114102 -1.432604  0.304340
953 -2.210788  0.834261 -1.790993  0.421272 -1.301499 -0.508087 -0.600657
365 -0.079718 -0.263842  0.747338  0.929554  0.426811 -1.590757  1.366447

           X0        X1
979  0.480911 -0.857583
786 -1.106383 -0.548760
809  0.909066 -0.559294
941  0.680252 -0.542851
342  0.242173 -1.379694
..        ...       ...
371  0.274353  0.338137
299 -0.272634 -1.227923
234  0.304340 -1.432604
953 -0.600657 -0.508087
365  1.366447 -1.590757

[800 rows x 9 columns], 'y': 979    10.393456
786     8.954078
809     7.084273
941     9.630585
342    -6.856146
         ...
371     4.468847
299     5.614664
234     7.980017
953    -9.484729
365    15.195618
Name: y, Length: 800, dtype: float64, 'treatment': 979     True
786     True
809     True
941     True
342    False
       ...
371     True
299     True
234     True
953    False
365     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1210
INFO:causalml:    RMSE (Treatment):     0.6654
INFO:causalml:   sMAPE   (Control):     0.5705
INFO:causalml:   sMAPE (Treatment):     0.1382
INFO:causalml:    Gini   (Control):     0.7480
INFO:causalml:    Gini (Treatment):     0.9959
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0215
INFO:causalml:    RMSE (Treatment):     0.6435
INFO:causalml:   sMAPE   (Control):     0.5569
INFO:causalml:   sMAPE (Treatment):     0.1369
INFO:causalml:    Gini   (Control):     0.7462
INFO:causalml:    Gini (Treatment):     0.9960

{'X':            W4        W2        W1        W3        W0        X1        X0  \
158 -0.957173  1.371619 -0.480406  1.601165 -1.831360 -1.142136  2.213026
970 -1.394364  1.786996  1.473136 -0.626023 -0.659760 -1.513833 -0.140064
299 -1.370319  0.104371 -0.773003  1.741095  0.119556 -1.227923 -0.272634
338  0.024125  2.398870 -0.344341  0.049499 -0.135206 -1.931463  1.292749
490 -2.391954  2.550940 -0.776817  0.245312  0.292239  1.475404  1.305845
..        ...       ...       ...       ...       ...       ...       ...
875 -2.411078  0.159398  0.119580  0.748787 -0.457547  0.313736 -0.560625
940  1.618120 -0.305935 -1.002070  0.408436 -0.407028 -1.124055  0.103301
38  -0.603358  0.428043 -1.045056 -0.064250 -1.028287 -0.614795  2.905953
528 -1.220354  1.951022 -0.143826  0.399718 -1.725250  0.579914  0.826622
126 -0.113592  2.137890  0.587189 -0.608982 -0.434552 -1.535190  0.152988

           X0        X1
158  2.213026 -1.142136
970 -0.140064 -1.513833
299 -0.272634 -1.227923
338  1.292749 -1.931463
490  1.305845  1.475404
..        ...       ...
875 -0.560625  0.313736
940  0.103301 -1.124055
38   2.905953 -0.614795
528  0.826622  0.579914
126  0.152988 -1.535190

[800 rows x 9 columns], 'y': 158    12.243766
970     3.837965
299     5.614664
338    14.613827
490    -3.562790
         ...
875     1.205333
940    12.297833
38     15.173396
528     8.156158
126     9.175909
Name: y, Length: 800, dtype: float64, 'treatment': 158     True
970     True
299     True
338     True
490    False
       ...
875     True
940     True
38      True
528     True
126     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
981 -1.677948 -1.622248 -1.171652  0.349836 -2.213015  1.101661  0.722546
610 -0.711808  1.595194  0.094204  0.005941 -1.302585 -0.669101  1.030998
356 -1.087973  1.063452 -0.582690 -0.391019 -0.717769  0.567377  0.968256
829 -1.396963 -0.080609 -2.047189 -0.910429 -1.164537 -1.251936 -0.347075
756  0.646687  0.512998 -0.425760  0.363156 -1.685429 -1.135889  1.931878
..        ...       ...       ...       ...       ...       ...       ...
454 -0.487418  0.412049 -0.859189  0.620612 -1.830073 -0.234510  2.177056
489 -0.365082 -1.451807 -0.737839  0.617674  0.183613 -0.899562 -1.117287
917 -2.304123  1.615978  1.217869  0.195346 -0.128304 -2.287419  1.444336
371 -2.437457  1.813953 -0.635430 -0.294641 -0.408002  0.338137  0.274353
936 -0.482737 -0.826928 -0.542605 -0.831002 -1.069918 -2.562988  1.258835

           X0        X1
981  0.722546  1.101661
610  1.030998 -0.669101
356  0.968256  0.567377
829 -0.347075 -1.251936
756  1.931878 -1.135889
..        ...       ...
454  2.177056 -0.234510
489 -1.117287 -0.899562
917  1.444336 -2.287419
371  0.274353  0.338137
936  1.258835 -2.562988

[800 rows x 9 columns], 'y': 981   -12.765797
610     9.248446
356    -4.713297
829    -9.531238
756    13.898148
         ...
454    12.121477
489     2.992182
917    -4.386132
371     4.468847
936     4.998172
Name: y, Length: 800, dtype: float64, 'treatment': 981    False
610     True
356    False
829    False
756     True
       ...
454     True
489     True
917    False
371     True
936     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0191
INFO:causalml:    RMSE (Treatment):     0.7000
INFO:causalml:   sMAPE   (Control):     0.5560
INFO:causalml:   sMAPE (Treatment):     0.1486
INFO:causalml:    Gini   (Control):     0.7625
INFO:causalml:    Gini (Treatment):     0.9959
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0636
INFO:causalml:    RMSE (Treatment):     0.7485

{'X':            W4        W2        W1        W3        W0        X1        X0  \
860 -0.100403  1.765904 -1.580557 -0.337570  0.115587 -0.811085 -0.628878
897 -0.501089 -1.031997 -1.334448 -0.012652 -0.451207 -0.315850 -0.369898
758 -0.238995  0.258464 -0.815419  0.855917 -0.016059 -1.265210  1.154044
0   -1.124599  0.284335 -0.838580  1.223258 -0.089772 -0.633368 -1.372310
521 -1.235550  2.140683 -2.152255  0.310299 -1.837627 -0.705293 -0.465131
..        ...       ...       ...       ...       ...       ...       ...
818 -2.294772  1.081263 -0.110948  0.240523 -2.452061 -0.676310  1.024324
822 -0.308029  1.239229 -1.712106  2.618474 -0.731033 -0.360836  1.878272
540 -1.436398  1.926302 -0.447886 -0.040584  2.181020  0.720121  1.462581
383 -1.820459  0.011031 -1.003686 -0.301110  0.254573 -1.724935  0.863482
708 -0.500479  1.096561  0.068238 -0.285794 -0.225941 -1.796636  1.564528

           X0        X1
860 -0.628878 -0.811085
897 -0.369898 -0.315850
758  1.154044 -1.265210
0   -1.372310 -0.633368
521 -0.465131 -0.705293
..        ...       ...
818  1.024324 -0.676310
822  1.878272 -0.360836
540  1.462581  0.720121
383  0.863482 -1.724935
708  1.564528 -1.796636

[800 rows x 9 columns], 'y': 860     7.407446
897    -4.566144
758    12.802985
0       2.034000
521     0.541061
         ...
818   -11.770683
822    17.217532
540    19.517995
383     5.450868
708    12.369009
Name: y, Length: 800, dtype: float64, 'treatment': 860     True
897    False
758     True
0       True
521     True
       ...
818    False
822     True
540     True
383     True
708     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
504 -1.822921  2.092858  0.863527 -0.530852 -1.148241 -0.820791  0.423434
634 -0.085908  0.582399 -1.871995 -0.128561  1.399274 -2.013647 -0.399001
127 -1.184442  1.633850 -0.489806  0.806896 -0.654125 -0.324203 -1.297638
542  0.627854  2.166200 -1.384347  1.110795 -0.604388  0.513683 -0.468804
370 -1.330248  0.617371 -1.086532  0.939956 -1.523047 -0.029272  0.894390
..        ...       ...       ...       ...       ...       ...       ...
793 -0.358699  1.859517 -0.250568 -1.297332 -1.747403 -0.402763  1.328310
365 -0.079718 -0.263842  0.747338  0.929554  0.426811 -1.590757  1.366447
608  0.655518  2.888275 -1.833586 -1.459779  0.528316  1.313325 -0.562842
781 -1.133150  1.232298 -0.515253  0.174776 -0.131612 -1.374035  0.690224
649 -2.154564  1.280369 -0.834181  1.662134 -0.490512  0.051132  1.668837

           X0        X1
504  0.423434 -0.820791
634 -0.399001 -2.013647
127 -1.297638 -0.324203
542 -0.468804  0.513683
370  0.894390 -0.029272
..        ...       ...
793  1.328310 -0.402763
365  1.366447 -1.590757
608 -0.562842  1.313325
781  0.690224 -1.374035
649  1.668837  0.051132

[800 rows x 9 columns], 'y': 504     4.307409
634     8.990907
127     2.218793
542    12.169721
370     6.533270
         ...
793     9.029191
365    15.195618
608    13.248344
781     8.454186
649    11.515556
Name: y, Length: 800, dtype: float64, 'treatment': 504    True
634    True
127    True
542    True
370    True
       ...
793    True
365    True
608    True
781    True
649    True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:   sMAPE   (Control):     0.5449
INFO:causalml:   sMAPE (Treatment):     0.1569
INFO:causalml:    Gini   (Control):     0.7369
INFO:causalml:    Gini (Treatment):     0.9940
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0360
INFO:causalml:    RMSE (Treatment):     0.6878
INFO:causalml:   sMAPE   (Control):     0.5334
INFO:causalml:   sMAPE (Treatment):     0.1338
INFO:causalml:    Gini   (Control):     0.7495
INFO:causalml:    Gini (Treatment):     0.9950
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
175 -1.154340  0.806588 -0.662689  0.887440  0.111731 -1.009443  2.862545
357 -0.393043 -0.896794 -0.679593 -0.767907 -0.184818  0.503640 -0.556493
833  0.105901  0.316176  0.723477  1.307736 -0.792919 -1.127981  1.166249
189  0.449357  0.147778  1.294543 -0.691706 -3.244261 -0.423599  0.935502
544 -1.005914  0.255475 -1.627301  1.838873  0.013614 -1.474838  1.050279
..        ...       ...       ...       ...       ...       ...       ...
289  0.772794  0.012983 -0.390292  0.616515  1.060846 -1.481334  1.630755
695 -0.197821 -0.512162  0.067361  0.935517 -0.381064 -1.154606  0.317349
677 -0.926078  0.166307 -2.183101  1.988610 -0.286812 -1.952046  0.131363
607 -0.713550 -0.806284 -0.339457 -0.279553 -0.477920 -0.690686 -0.221325
334 -0.156694  0.911286  0.765684  0.401905 -1.416657 -2.667282 -1.314160

           X0        X1
175  2.862545 -1.009443
357 -0.556493  0.503640
833  1.166249 -1.127981
189  0.935502 -0.423599
544  1.050279 -1.474838
..        ...       ...
289  1.630755 -1.481334
695  0.317349 -1.154606
677  0.131363 -1.952046
607 -0.221325 -0.690686
334 -1.314160 -2.667282

[800 rows x 9 columns], 'y': 175    17.721185
357     4.629609
833    13.445887
189     5.702358
544    10.741956
         ...
289    19.767563
695     8.766754
677     6.044637
607     3.571733
334    -0.271330
Name: y, Length: 800, dtype: float64, 'treatment': 175    True
357    True
833    True
189    True
544    True
       ...
289    True
695    True
677    True
607    True
334    True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0888
INFO:causalml:    RMSE (Treatment):     0.7248
INFO:causalml:   sMAPE   (Control):     0.5474
INFO:causalml:   sMAPE (Treatment):     0.1538
INFO:causalml:    Gini   (Control):     0.7380
INFO:causalml:    Gini (Treatment):     0.9949
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0995
INFO:causalml:    RMSE (Treatment):     0.7069
INFO:causalml:   sMAPE   (Control):     0.5420
INFO:causalml:   sMAPE (Treatment):     0.1515
INFO:causalml:    Gini   (Control):     0.7201
INFO:causalml:    Gini (Treatment):     0.9951
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0859
INFO:causalml:    RMSE (Treatment):     0.7073

{'X':            W4        W2        W1        W3        W0        X1        X0  \
228 -0.479531  0.804877 -0.334062  0.447143  0.576800 -0.277841 -0.766331
866 -2.492060  0.007731 -0.940262 -0.496057  1.104068  0.043952  0.520345
851 -1.580498  2.916707 -1.305058  0.877563  0.733961 -1.876975  2.623342
795 -1.189871  1.612262 -1.396546 -0.154045  0.178844  0.278181 -0.852625
241 -0.810324  2.347069 -0.803031  1.814552  0.121529 -2.824841 -0.307345
..        ...       ...       ...       ...       ...       ...       ...
583 -0.820218 -0.945137 -1.568877 -0.082810  0.614237 -0.346996  0.919443
74  -0.996694  0.160173 -2.637286 -2.023282  1.303478 -1.990375  1.207325
453 -1.249839 -0.644978 -0.359153  1.084902 -0.516596 -0.365041 -0.139304
723 -0.107996  1.393754  0.825508  0.735281 -0.586520 -0.348836  0.538970
874  0.534013  2.308714  0.793617  1.992239 -1.610429 -0.414126  1.030813

           X0        X1
228 -0.766331 -0.277841
866  0.520345  0.043952
851  2.623342 -1.876975
795 -0.852625  0.278181
241 -0.307345 -2.824841
..        ...       ...
583  0.919443 -0.346996
74   1.207325 -1.990375
453 -0.139304 -0.365041
723  0.538970 -0.348836
874  1.030813 -0.414126

[800 rows x 9 columns], 'y': 228     8.255998
866     6.414822
851    18.155148
795     5.028396
241     7.793042
         ...
583    -2.819370
74      8.688916
453     4.442757
723    12.592456
874    16.052226
Name: y, Length: 800, dtype: float64, 'treatment': 228     True
866     True
851     True
795     True
241     True
       ...
583    False
74      True
453     True
723     True
874     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
451 -0.565758  1.128663 -0.324990 -0.337899 -0.015063  0.346414  0.109342
945  0.033044  1.015786  0.124006 -0.242485 -0.539339 -2.297596  1.009465
145 -2.815317  1.548436 -0.624088  0.844806  0.438846  0.070108 -0.126092
777 -0.500132  1.841908  0.773237 -0.146416 -0.489405 -1.162506  1.188357
422 -1.830252  0.894077 -0.267716 -0.367326  2.202751 -0.079619  0.119763
..        ...       ...       ...       ...       ...       ...       ...
293 -2.019915 -0.918739 -1.767926 -1.403425 -0.740373 -1.576824  0.279021
84  -1.231904  0.946563 -0.520574 -1.241545 -1.073605  0.279123  0.201559
549  1.093852  0.562148 -1.545915 -0.970186 -0.027322  0.044818  1.075594
315 -0.753510  0.301264  1.390165  1.628534  0.274823 -0.437217  0.126621
426 -0.479176  1.237188 -0.726685 -0.314696  0.767152 -0.740509 -0.373064

           X0        X1
451  0.109342  0.346414
945  1.009465 -2.297596
145 -0.126092  0.070108
777  1.188357 -1.162506
422  0.119763 -0.079619
..        ...       ...
293  0.279021 -1.576824
84   0.201559  0.279123
549  1.075594  0.044818
315  0.126621 -0.437217
426 -0.373064 -0.740509

[800 rows x 9 columns], 'y': 451     9.760052
945    10.461920
145    -4.715614
777    12.450890
422    11.141529
         ...
293   -11.609205
84     -7.210816
549    15.679880
315    11.545213
426     8.947595
Name: y, Length: 800, dtype: float64, 'treatment': 451     True
945     True
145    False
777     True
422     True
       ...
293    False
84     False
549     True
315     True
426     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
174 -1.209450  2.573548 -1.875041  0.466606 -2.593907 -0.787080  0.157327
660 -1.323851  1.441421  0.356298 -1.619747  1.211768 -0.134022  0.702311
480 -0.377755  0.312070  0.399832  1.532559  0.348411  0.225440  0.753623
919  0.927871  1.119628 -0.747494 -0.682620  0.435117 -2.887356  0.670618
774 -0.770624  0.071105  0.522571 -0.979329  0.197455 -1.304148  0.066355
..        ...       ...       ...       ...       ...       ...       ...
875 -2.411078  0.159398  0.119580  0.748787 -0.457547  0.313736 -0.560625
568  0.513430  0.522040  0.017360 -0.306262 -0.660055 -1.021097  1.297397
375 -1.851590 -0.485257 -0.505663 -0.368564 -1.023961 -1.380998  1.320727
106 -1.469398  1.456738  0.120477 -0.396731 -2.091289 -1.960327  0.856698
713 -0.587834  2.036860 -1.408772  1.303268 -0.384541 -0.825095 -0.057160

           X0        X1
174  0.157327 -0.787080
660  0.702311 -0.134022
480  0.753623  0.225440
919  0.670618 -2.887356
774  0.066355 -1.304148
..        ...       ...
875 -0.560625  0.313736
568  1.297397 -1.021097
375  1.320727 -1.380998
106  0.856698 -1.960327
713 -0.057160 -0.825095

[800 rows x 9 columns], 'y': 174     1.619585
660    11.491007
480    15.267030
919    12.794015
774     6.080336
         ...
875     1.205333
568    13.469854
375     3.740574
106    -8.631314
713     9.135870
Name: y, Length: 800, dtype: float64, 'treatment': 174     True
660     True
480     True
919     True
774     True
       ...
875     True
568     True
375     True
106    False
713     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:   sMAPE   (Control):     0.5643
INFO:causalml:   sMAPE (Treatment):     0.1448
INFO:causalml:    Gini   (Control):     0.7575
INFO:causalml:    Gini (Treatment):     0.9950
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0283
INFO:causalml:    RMSE (Treatment):     0.7092
INFO:causalml:   sMAPE   (Control):     0.5471
INFO:causalml:   sMAPE (Treatment):     0.1393
INFO:causalml:    Gini   (Control):     0.7309
INFO:causalml:    Gini (Treatment):     0.9946
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0752
INFO:causalml:    RMSE (Treatment):     0.7231
INFO:causalml:   sMAPE   (Control):     0.5220
INFO:causalml:   sMAPE (Treatment):     0.1459

{'X':            W4        W2        W1        W3        W0        X1        X0  \
275 -2.120068 -0.318202  0.171676  1.909965 -0.392065 -1.480635 -1.148364
941 -0.858300  2.689664  0.060630 -0.800292 -0.649474 -0.542851  0.680252
637 -1.283359  0.832725  0.002037 -1.432698 -2.375726 -1.559718  1.057731
132 -4.155165  1.496680 -0.815922  1.278145 -1.973326  0.148467  0.226076
667 -2.198198  0.869380  0.049001  1.338380 -0.046996  0.313350  1.524353
..        ...       ...       ...       ...       ...       ...       ...
775 -2.360643  1.573135 -1.344099  0.823569 -1.137799 -0.227823  0.357754
53  -2.930083  0.879228 -0.579348  0.759465 -0.462352 -2.221874  2.337682
208 -1.829170  0.787526 -0.881231  0.083780 -1.917817 -2.457417  0.754683
157 -1.514941  0.601247 -2.511296  0.377276  0.269567 -0.229923  0.816437
404  0.269959 -1.426995 -0.624877  1.207414 -0.632554  0.826927 -0.342909

           X0        X1
275 -1.148364 -1.480635
941  0.680252 -0.542851
637  1.057731 -1.559718
132  0.226076  0.148467
667  1.524353  0.313350
..        ...       ...
775  0.357754 -0.227823
53   2.337682 -2.221874
208  0.754683 -2.457417
157  0.816437 -0.229923
404 -0.342909  0.826927

[800 rows x 9 columns], 'y': 275    -1.163599
941     9.630585
637   -10.850731
132    -3.629107
667    11.985876
         ...
775    -7.934090
53      7.558222
208    -9.928296
157     8.671216
404    -1.323791
Name: y, Length: 800, dtype: float64, 'treatment': 275     True
941     True
637    False
132     True
667     True
       ...
775    False
53      True
208    False
157     True
404    False
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
49  -0.982952  2.225363 -1.526931 -0.694241  0.029243 -0.455032  0.234971
682 -0.223833  1.310405 -0.330832  0.125670  1.368049  0.141310 -0.773242
280 -0.924446 -0.495998 -1.067446 -0.475160 -0.560881 -1.593906  1.057462
220  0.757100  1.090061 -0.251709  0.571761 -1.084509 -1.752894  0.512210
862 -2.626160 -0.085283  0.861203  0.598807  0.007442 -1.089904 -0.141363
..        ...       ...       ...       ...       ...       ...       ...
269  0.573973 -0.053532 -0.674589 -3.004365  1.021180 -0.146757  0.753673
194 -1.962419  0.749901 -0.124856  1.434591 -0.397338 -0.555662  0.763135
555 -0.854260 -0.912091 -1.765080  1.636757 -1.353961 -2.104020  1.321314
987 -0.812298  1.417451 -0.621462 -0.360324 -1.103340 -1.337427  0.384151
893 -1.491824  0.452183 -1.205184  0.340149  1.739224 -0.632809  0.210046

           X0        X1
49   0.234971 -0.455032
682 -0.773242  0.141310
280  1.057462 -1.593906
220  0.512210 -1.752894
862 -0.141363 -1.089904
..        ...       ...
269  0.753673 -0.146757
194  0.763135 -0.555662
555  1.321314 -2.104020
987  0.384151 -1.337427
893  0.210046 -0.632809

[800 rows x 9 columns], 'y': 49      8.274390
682    11.702839
280     5.994137
220    10.878908
862     1.585107
         ...
269     0.249321
194     7.805228
555    -6.061451
987    -4.527449
893    10.444157
Name: y, Length: 800, dtype: float64, 'treatment': 49      True
682     True
280     True
220     True
862     True
       ...
269    False
194     True
555    False
987    False
893     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    Gini   (Control):     0.7268
INFO:causalml:    Gini (Treatment):     0.9948
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1012
INFO:causalml:    RMSE (Treatment):     0.6904
INFO:causalml:   sMAPE   (Control):     0.5517
INFO:causalml:   sMAPE (Treatment):     0.1317
INFO:causalml:    Gini   (Control):     0.7215
INFO:causalml:    Gini (Treatment):     0.9953
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0529
INFO:causalml:    RMSE (Treatment):     0.6916
INFO:causalml:   sMAPE   (Control):     0.5471

{'X':            W4        W2        W1        W3        W0        X1        X0  \
965 -0.258222  2.196243 -1.509052  0.242610 -0.852991 -0.125788  3.020499
18  -1.721390  0.991295 -1.008932  1.539819 -0.923055 -0.609885 -0.196033
816 -0.262987  0.921280 -0.389069 -0.883456 -2.158824 -1.767588 -0.312548
373 -0.576617  0.832892 -0.252974  2.508469 -0.393241  0.636110  0.102158
39  -0.961481  1.275170 -1.485334 -0.763943 -1.116911  0.131391  1.712740
..        ...       ...       ...       ...       ...       ...       ...
127 -1.184442  1.633850 -0.489806  0.806896 -0.654125 -0.324203 -1.297638
798  0.298268  1.287738 -0.339625  0.484676 -1.361646 -0.844488  0.584065
240 -1.161647  1.005936  0.248576  1.135152 -0.087861 -0.969873 -0.362578
319 -0.246796  1.042340  0.473929 -1.882334 -1.252649 -0.129804 -2.165228
86  -1.171030  0.999708 -0.474575 -0.002353  1.214732 -0.432952 -1.419492

           X0        X1
965  3.020499 -0.125788
18  -0.196033 -0.609885
816 -0.312548 -1.767588
373  0.102158  0.636110
39   1.712740  0.131391
..        ...       ...
127 -1.297638 -0.324203
798  0.584065 -0.844488
240 -0.362578 -0.969873
319 -2.165228 -0.129804
86  -1.419492 -0.432952

[800 rows x 9 columns], 'y': 965    19.735711
18     -5.046192
816    -0.020121
373    12.268099
39     -6.109185
         ...
127     2.218793
798    10.206839
240     6.512254
319    -2.951778
86      4.893758
Name: y, Length: 800, dtype: float64, 'treatment': 965     True
18     False
816     True
373     True
39     False
       ...
127     True
798     True
240     True
319     True
86      True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
355 -0.326494  2.433717  0.080853  1.915810 -0.164674  0.466589 -0.152236
586 -1.822525  1.328268 -0.580694  0.434084 -1.937340  0.184012  3.008666
116 -1.086365 -0.990762  0.079434  1.325440  1.224208  0.738869  0.436850
631  0.271998  2.338601 -1.112341  1.567964 -0.208038 -0.964256  0.333245
94  -0.994409 -0.051791 -0.396042  0.809534  0.517539 -0.681357  0.730109
..        ...       ...       ...       ...       ...       ...       ...
227 -0.757316  2.009235 -0.832385  1.297304 -0.017571 -1.484499  0.073045
643 -0.837543  2.690282 -0.956587  1.519004 -3.148478  1.188059  1.128666
834 -0.921188  0.601325 -1.805151  0.607381  0.656701  0.552350  0.119174
646 -0.626251  1.021268  0.186793  1.691995 -1.534219 -0.559809  0.720521
530 -1.096231 -0.378584 -1.082200  0.789542 -0.662083 -1.309586  1.838881

           X0        X1
355 -0.152236  0.466589
586  3.008666  0.184012
116  0.436850  0.738869
631  0.333245 -0.964256
94   0.730109 -0.681357
..        ...       ...
227  0.073045 -1.484499
643  1.128666  1.188059
834  0.119174  0.552350
646  0.720521 -0.559809
530  1.838881 -1.309586

[800 rows x 9 columns], 'y': 355    13.712619
586    -8.767996
116    13.066025
631    14.220450
94     10.995359
         ...
227     9.555874
643     9.118838
834    10.523346
646     9.399353
530    10.101463
Name: y, Length: 800, dtype: float64, 'treatment': 355     True
586    False
116     True
631     True
94      True
       ...
227     True
643     True
834     True
646     True
530     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:   sMAPE (Treatment):     0.1404
INFO:causalml:    Gini   (Control):     0.7361
INFO:causalml:    Gini (Treatment):     0.9952
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0703
INFO:causalml:    RMSE (Treatment):     0.6898
INFO:causalml:   sMAPE   (Control):     0.5459
INFO:causalml:   sMAPE (Treatment):     0.1425
INFO:causalml:    Gini   (Control):     0.7482
INFO:causalml:    Gini (Treatment):     0.9953
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1016
INFO:causalml:    RMSE (Treatment):     0.7318
INFO:causalml:   sMAPE   (Control):     0.5510
INFO:causalml:   sMAPE (Treatment):     0.1584
INFO:causalml:    Gini   (Control):     0.7415
INFO:causalml:    Gini (Treatment):     0.9951
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
705  0.261741  3.188563 -2.010226 -0.517940  1.029141  0.040541  0.750995
44   0.360294  0.124152 -2.197239 -1.328035 -0.866502 -0.042365  0.714713
738 -0.532602  1.680468 -1.570149  2.071328 -0.883423 -2.507779  0.510461
359 -1.250892  0.608337 -1.159236  1.196291 -2.448155 -1.418204  2.171817
531 -1.304303 -0.065667 -1.069981  0.533144  1.279208 -0.691485 -0.259040
..        ...       ...       ...       ...       ...       ...       ...
275 -2.120068 -0.318202  0.171676  1.909965 -0.392065 -1.480635 -1.148364
128 -0.446814  1.132974 -0.280832 -0.265640  0.705652  0.138788  1.457893
633 -2.030034 -0.174624 -0.342485  0.323698 -1.221663  0.242803 -0.377460
502 -1.817353  2.122041 -2.913312  1.536276 -0.699921 -0.900995  1.371631
971 -1.011609  0.842579 -1.257641  1.094270  0.446977  1.836771  1.109406

           X0        X1
705  0.750995  0.040541
44   0.714713 -0.042365
738  0.510461 -2.507779
359  2.171817 -1.418204
531 -0.259040 -0.691485
..        ...       ...
275 -1.148364 -1.480635
128  1.457893  0.138788
633 -0.377460  0.242803
502  1.371631 -0.900995
971  1.109406  1.836771

[800 rows x 9 columns], 'y': 705    18.039614
44      8.606075
738     8.470271
359     7.556557
531     7.735845
         ...
275    -1.163599
128    16.791480
633    -0.246762
502     9.319528
971    16.046613
Name: y, Length: 800, dtype: float64, 'treatment': 705    True
44     True
738    True
359    True
531    True
       ...
275    True
128    True
633    True
502    True
971    True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
75  -0.681840 -0.860902 -0.003540  0.004867 -0.711607  1.445133  2.210988
791 -0.302223 -0.035221 -0.779010 -1.067815 -1.088018  1.298864  1.837893
668 -0.124828  0.334842  0.337000 -0.098033 -0.948690 -0.993841  1.156376
861 -2.010612 -1.734882 -0.315201  0.998088  0.426792 -0.740070  0.178178
708 -0.500479  1.096561  0.068238 -0.285794 -0.225941 -1.796636  1.564528
..        ...       ...       ...       ...       ...       ...       ...
491  0.047326  0.391397 -0.404472  0.060057  0.642744 -0.800913 -0.652640
8   -1.192098  0.543069  0.448059  1.496816  1.148883 -0.101334  0.352319
332 -1.250673  0.380127 -0.875099  1.332656  0.562342  0.345025  1.790799
202 -1.442253  1.720388  1.836666  1.221145 -0.572915 -1.254973 -1.342614
890 -1.222633  2.135828 -0.734609 -0.488569 -0.600416 -0.479990 -0.460961

           X0        X1
75   2.210988  1.445133
791  1.837893  1.298864
668  1.156376 -0.993841
861  0.178178 -0.740070
708  1.564528 -1.796636
..        ...       ...
491 -0.652640 -0.800913
8    0.352319 -0.101334
332  1.790799  0.345025
202 -1.342614 -1.254973
890 -0.460961 -0.479990

[800 rows x 9 columns], 'y': 75     -4.842126
791    12.700214
668    10.579344
861     4.083148
708    12.369009
         ...
491     8.770425
8      13.374297
332    16.296656
202     2.297590
890    -3.809211
Name: y, Length: 800, dtype: float64, 'treatment': 75     False
791     True
668     True
861     True
708     True
       ...
491     True
8       True
332     True
202     True
890    False
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9884
INFO:causalml:    RMSE (Treatment):     0.6927
INFO:causalml:   sMAPE   (Control):     0.5061
INFO:causalml:   sMAPE (Treatment):     0.1410
INFO:causalml:    Gini   (Control):     0.7191
INFO:causalml:    Gini (Treatment):     0.9945
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9921
INFO:causalml:    RMSE (Treatment):     0.7493
INFO:causalml:   sMAPE   (Control):     0.5346
INFO:causalml:   sMAPE (Treatment):     0.1501
INFO:causalml:    Gini   (Control):     0.7535
INFO:causalml:    Gini (Treatment):     0.9943
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0065
INFO:causalml:    RMSE (Treatment):     0.6871
INFO:causalml:   sMAPE   (Control):     0.5569
INFO:causalml:   sMAPE (Treatment):     0.1468
INFO:causalml:    Gini   (Control):     0.7082
INFO:causalml:    Gini (Treatment):     0.9952
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
84  -1.231904  0.946563 -0.520574 -1.241545 -1.073605  0.279123  0.201559
920 -2.817866  0.619407 -1.383961 -0.429572  0.136050 -0.313979  1.711518
406 -1.787177  0.601264 -0.241539 -0.288932  0.204519  1.070860  0.778961
841 -0.646074 -0.355693 -1.037555  1.155296 -0.106020 -1.428374  0.724408
700 -1.952536 -0.329336 -1.215430  0.057645 -0.943685 -0.856869  2.159186
..        ...       ...       ...       ...       ...       ...       ...
88   0.116208  0.595981 -0.004589  2.371107  0.664072  1.005779  0.675959
703 -0.880997 -1.820627  0.171736 -0.167854  0.599802 -2.011412 -0.082251
787  0.106762  2.948496 -1.895033 -1.510659 -0.813685 -0.585402 -0.112157
565  0.489147  1.219821 -1.070019  0.305099  0.624808 -0.830622  1.045480
672 -1.285233  1.492835 -0.461824 -1.224488 -2.543523 -0.472754  1.225212

           X0        X1
84   0.201559  0.279123
920  1.711518 -0.313979
406  0.778961  1.070860
841  0.724408 -1.428374
700  2.159186 -0.856869
..        ...       ...
88   0.675959  1.005779
703 -0.082251 -2.011412
787 -0.112157 -0.585402
565  1.045480 -0.830622
672  1.225212 -0.472754

[800 rows x 9 columns], 'y': 84     -7.210816
920    -8.542729
406     9.585905
841     9.168319
700    -9.193834
         ...
88     19.338740
703    -3.147667
787     7.397373
565    17.014565
672     3.326896
Name: y, Length: 800, dtype: float64, 'treatment': 84     False
920    False
406     True
841     True
700    False
       ...
88      True
703    False
787     True
565     True
672     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
597 -0.321245  0.240069 -1.490242  2.231212 -0.904581  0.578349 -0.009237
904 -1.109443  3.107200 -0.951024 -0.079768  1.948789 -1.132518 -0.890302
290  0.299643 -1.028151  0.230684 -0.517070  0.924262 -1.450635  0.759954
909 -0.448326  1.299826  0.401639 -0.500486 -1.006631 -0.906303  1.442992
875 -2.411078  0.159398  0.119580  0.748787 -0.457547  0.313736 -0.560625
..        ...       ...       ...       ...       ...       ...       ...
453 -1.249839 -0.644978 -0.359153  1.084902 -0.516596 -0.365041 -0.139304
430 -0.536437 -0.421504 -0.788914 -1.566788 -0.855747 -0.483673  0.158809
116 -1.086365 -0.990762  0.079434  1.325440  1.224208  0.738869  0.436850
634 -0.085908  0.582399 -1.871995 -0.128561  1.399274 -2.013647 -0.399001
734 -2.534543  1.297053 -0.227526 -0.605377 -1.703765 -0.280342  0.415044

           X0        X1
597 -0.009237  0.578349
904 -0.890302 -1.132518
290  0.759954 -1.450635
909  1.442992 -0.906303
875 -0.560625  0.313736
..        ...       ...
453 -0.139304 -0.365041
430  0.158809 -0.483673
116  0.436850  0.738869
634 -0.399001 -2.013647
734  0.415044 -0.280342

[800 rows x 9 columns], 'y': 597    -1.143963
904    10.065464
290    12.664366
909    11.235374
875     1.205333
         ...
453     4.442757
430     3.344323
116    13.066025
634     8.990907
734    -0.213830
Name: y, Length: 800, dtype: float64, 'treatment': 597    False
904     True
290     True
909     True
875     True
       ...
453     True
430     True
116     True
634     True
734     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
90   0.097345  0.533868 -1.112249  0.999274  0.230117 -1.125698  0.717416
686 -1.377233 -0.617476 -0.353271  2.000711 -1.831548  0.632213  0.262429
240 -1.161647  1.005936  0.248576  1.135152 -0.087861 -0.969873 -0.362578
511  0.263820  1.872437 -0.939853  1.755917 -1.252826  1.759191  0.714215
622 -0.848690 -0.845794  0.267025 -0.137135  0.356153 -0.001756  0.376872
..        ...       ...       ...       ...       ...       ...       ...
671 -1.805567  1.966280  0.355705  0.675031 -1.531579 -0.217720 -0.701737
964  0.753382  0.592423 -0.618551  0.045706 -0.493502 -1.581298 -1.715736
299 -1.370319  0.104371 -0.773003  1.741095  0.119556 -1.227923 -0.272634
572 -2.548463 -0.098732 -0.957317  0.639523 -0.249403  1.104659 -0.047994
754 -1.008916  0.089333 -0.381037  1.182553  0.653293 -0.475920  0.036262

           X0        X1
90   0.717416 -1.125698
686  0.262429  0.632213
240 -0.362578 -0.969873
511  0.714215  1.759191
622  0.376872 -0.001756
..        ...       ...
671 -0.701737 -0.217720
964 -1.715736 -1.581298
299 -0.272634 -1.227923
572 -0.047994  1.104659
754  0.036262 -0.475920

[800 rows x 9 columns], 'y': 90     13.341932
686     4.432389
240     6.512254
511    15.928076
622     8.845133
         ...
671    -6.150284
964     3.134159
299     5.614664
572    -8.024569
754     9.673575
Name: y, Length: 800, dtype: float64, 'treatment': 90      True
686     True
240     True
511     True
622     True
       ...
671    False
964     True
299     True
572    False
754     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0987
INFO:causalml:    RMSE (Treatment):     0.7208
INFO:causalml:   sMAPE   (Control):     0.5237
INFO:causalml:   sMAPE (Treatment):     0.1429
INFO:causalml:    Gini   (Control):     0.7470
INFO:causalml:    Gini (Treatment):     0.9951
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0540
INFO:causalml:    RMSE (Treatment):     0.7624
INFO:causalml:   sMAPE   (Control):     0.5464
INFO:causalml:   sMAPE (Treatment):     0.1583
INFO:causalml:    Gini   (Control):     0.7329
INFO:causalml:    Gini (Treatment):     0.9945
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1489
INFO:causalml:    RMSE (Treatment):     0.7569
INFO:causalml:   sMAPE   (Control):     0.5456
INFO:causalml:   sMAPE (Treatment):     0.1493
INFO:causalml:    Gini   (Control):     0.7273
INFO:causalml:    Gini (Treatment):     0.9940

{'X':            W4        W2        W1        W3        W0        X1        X0  \
334 -0.156694  0.911286  0.765684  0.401905 -1.416657 -2.667282 -1.314160
262 -1.630045 -1.251623 -0.321312  1.248217  0.588834 -0.498320  0.369726
674 -1.388549  1.485270 -0.646514 -0.090141  1.767213  0.077538 -0.650185
955 -0.372062  0.536404 -1.497404  1.331291  1.949449 -0.806243  1.113560
832 -0.682884  1.926730 -0.161279 -0.512041 -0.170792 -1.586715  0.243828
..        ...       ...       ...       ...       ...       ...       ...
28   0.355363 -1.712344 -0.541527 -1.500008 -0.178104 -0.523930 -0.313656
582 -1.856578  1.519025 -0.354817  1.777607 -0.103456 -0.540906  0.894554
294  0.718662  0.293405 -0.329930  0.322284 -0.725980 -1.672422  0.046675
846 -1.672185 -0.347375  0.787428  0.336686  0.622999  0.804101  1.251836
716 -1.737178  1.386498 -1.564507 -1.024549  1.582414 -0.183054  0.305977

           X0        X1
334 -1.314160 -2.667282
262  0.369726 -0.498320
674 -0.650185  0.077538
955  1.113560 -0.806243
832  0.243828 -1.586715
..        ...       ...
28  -0.313656 -0.523930
582  0.894554 -0.540906
294  0.046675 -1.672422
846  1.251836  0.804101
716  0.305977 -0.183054

[800 rows x 9 columns], 'y': 334    -0.271330
262     7.461448
674     9.459822
955    18.503792
832     7.911675
         ...
28      4.753158
582    10.549091
294     8.885443
846    12.705539
716     9.358799
Name: y, Length: 800, dtype: float64, 'treatment': 334    True
262    True
674    True
955    True
832    True
       ...
28     True
582    True
294    True
846    True
716    True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
475 -1.371406 -1.789706 -1.298206 -1.611236 -0.709442 -0.195347  2.724470
588 -0.930255 -0.378142  0.632255  0.279449 -1.818598 -1.226886  0.208515
279 -2.042622 -0.483061 -0.027763  0.310754  0.006903  0.717223  1.415660
206  0.034341 -1.189055 -0.976649  0.149847  1.575362 -0.130203  2.269800
952 -0.623857  0.509187 -1.613917 -0.527539 -0.021147 -1.638291  1.771485
..        ...       ...       ...       ...       ...       ...       ...
343 -1.245795  1.748387  0.828184 -1.775403 -0.972468 -0.866709  0.489782
843 -1.819689  2.321605  1.205835  1.378205 -0.687589 -1.421073 -0.008063
469 -1.955975  0.090817  0.294803 -2.001217 -0.945376 -1.628086  1.883115
369 -2.390398  0.327099 -0.741555 -1.805522 -0.865166 -1.456077 -2.343644
111  1.024268  0.556637 -0.514663  1.858435 -0.414872 -0.567619  0.793652

           X0        X1
475  2.724470 -0.195347
588  0.208515 -1.226886
279  1.415660  0.717223
206  2.269800 -0.130203
952  1.771485 -1.638291
..        ...       ...
343  0.489782 -0.866709
843 -0.008063 -1.421073
469  1.883115 -1.628086
369 -2.343644 -1.456077
111  0.793652 -0.567619

[800 rows x 9 columns], 'y': 475   -10.586297
588    -7.223205
279    -6.155145
206    20.680223
952    -2.868485
         ...
343     4.731928
843     6.032900
469     4.435916
369   -11.558437
111    16.735009
Name: y, Length: 800, dtype: float64, 'treatment': 475    False
588    False
279    False
206     True
952    False
       ...
343     True
843     True
469     True
369    False
111     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
899 -0.395984 -0.753509 -0.320954 -0.871588 -1.254406 -0.867205  0.087498
34   0.276106  2.330464  0.579228 -0.077358  0.414878  1.933343  1.404165
761 -1.783477 -0.235446 -0.829345 -1.351781  0.988020 -0.599609  1.020585
699 -1.175823  0.350680  0.033996  0.794509 -0.475092 -2.358432  0.259472
546 -2.888766  0.186649  0.030494  0.288355 -1.192114 -0.335307  1.980589
..        ...       ...       ...       ...       ...       ...       ...
72  -2.020604 -1.051226 -0.996921 -0.824436 -1.488663 -0.206553  0.489298
30  -1.029675 -0.036475  0.168119  0.799758  0.713907 -1.117296  0.492739
576 -0.258472  1.109831  0.044998  0.470214  0.134336 -1.293955  1.695302
678  0.508374  0.644654 -0.261807  0.486776 -1.449243  0.145155  1.107169
669 -0.015285  1.132134 -0.997982  0.382392  0.634601 -2.152286 -0.881791

           X0        X1
899  0.087498 -0.867205
34   1.404165  1.933343
761  1.020585 -0.599609
699  0.259472 -2.358432
546  1.980589 -0.335307
..        ...       ...
72   0.489298 -0.206553
30   0.492739 -1.117296
576  1.695302 -1.293955
678  1.107169  0.145155
669 -0.881791 -2.152286

[800 rows x 9 columns], 'y': 899     2.709571
34     22.166660
761     7.959767
699     4.759513
546   -11.053337
         ...
72    -12.586391
30     10.313749
576    16.021321
678    13.061989
669     6.961904
Name: y, Length: 800, dtype: float64, 'treatment': 899     True
34      True
761     True
699     True
546    False
       ...
72     False
30      True
576     True
678     True
669     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0130
INFO:causalml:    RMSE (Treatment):     0.7012
INFO:causalml:   sMAPE   (Control):     0.5313
INFO:causalml:   sMAPE (Treatment):     0.1381
INFO:causalml:    Gini   (Control):     0.7394
INFO:causalml:    Gini (Treatment):     0.9951
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True

{'X':            W4        W2        W1        W3        W0        X1        X0  \
946 -0.056748  1.082737  0.733627  1.643594 -0.787399 -1.668734  1.968422
838 -0.753922 -0.045816 -0.873461  1.047785 -0.824178 -1.028226 -0.543500
677 -0.926078  0.166307 -2.183101  1.988610 -0.286812 -1.952046  0.131363
805 -2.622025 -0.308385 -0.881486 -0.183280  0.289018 -2.986494  0.245462
75  -0.681840 -0.860902 -0.003540  0.004867 -0.711607  1.445133  2.210988
..        ...       ...       ...       ...       ...       ...       ...
790 -0.783165  1.865815 -0.439194  0.520276 -0.904192  0.041655  1.027622
410 -0.703120  0.450567 -0.504908  0.896033 -1.220663 -3.269095 -1.609582
968 -0.626450  0.610291 -0.847638  0.077436  0.480144 -1.839511  0.703036
615 -1.312910  0.549883 -0.743413  1.273369 -2.435562 -1.071918  2.090585
864 -1.315606  1.131888 -1.274700 -1.261592 -1.388695 -0.667723  0.600493

           X0        X1
946  1.968422 -1.668734
838 -0.543500 -1.028226
677  0.131363 -1.952046
805  0.245462 -2.986494
75   2.210988  1.445133
..        ...       ...
790  1.027622  0.041655
410 -1.609582 -3.269095
968  0.703036 -1.839511
615  2.090585 -1.071918
864  0.600493 -0.667723

[800 rows x 9 columns], 'y': 946    16.526801
838     3.145267
677     6.044637
805    -0.721618
75     -4.842126
         ...
790    -2.228632
410    -3.800252
968    10.038489
615    -8.516564
864    -8.539115
Name: y, Length: 800, dtype: float64, 'treatment': 946     True
838     True
677     True
805     True
75     False
       ...
790    False
410     True
968     True
615    False
864    False
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
595 -1.371638 -0.038092  0.472554  0.208273  0.608900 -0.921483  0.132164
488 -1.198321  1.515799  0.559743 -0.112272 -1.395849  1.313824  1.637690
140 -0.284186 -0.187325 -1.110371  1.711081 -1.545623  0.250207 -0.110677
318 -0.503101 -0.600895 -0.267185  0.021973  1.464131 -0.551488 -0.234670
863  0.652126  1.026512  0.144431  0.576424  1.624713 -2.511548  1.059346
..        ...       ...       ...       ...       ...       ...       ...
622 -0.848690 -0.845794  0.267025 -0.137135  0.356153 -0.001756  0.376872
601  0.106682 -0.437917 -2.078172  0.727072 -0.486981 -0.797348  0.454592
958 -0.495343  0.847053 -0.028871 -0.718100 -0.871586 -0.630299 -0.132512
519 -0.928551  1.643629 -0.012840 -0.089474 -1.289071 -0.902553  0.551816
393 -1.338523  0.522130  0.208298  1.121733 -0.869824 -0.203718 -0.158286

           X0        X1
595  0.132164 -0.921483
488  1.637690  1.313824
140 -0.110677  0.250207
318 -0.234670 -0.551488
863  1.059346 -2.511548
..        ...       ...
622  0.376872 -0.001756
601  0.454592 -0.797348
958 -0.132512 -0.630299
519  0.551816 -0.902553
393 -0.158286 -0.203718

[800 rows x 9 columns], 'y': 595    -1.962053
488    12.205540
140    -3.623800
318    10.068543
863    18.929095
         ...
622     8.845133
601    -1.707354
958     5.051010
519     6.513308
393    -4.149152
Name: y, Length: 800, dtype: float64, 'treatment': 595    False
488     True
140    False
318     True
863     True
       ...
622     True
601    False
958     True
519     True
393    False
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    RMSE   (Control):     2.9581
INFO:causalml:    RMSE (Treatment):     0.7019
INFO:causalml:   sMAPE   (Control):     0.5270
INFO:causalml:   sMAPE (Treatment):     0.1366
INFO:causalml:    Gini   (Control):     0.7395
INFO:causalml:    Gini (Treatment):     0.9947
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0089
INFO:causalml:    RMSE (Treatment):     0.6914
INFO:causalml:   sMAPE   (Control):     0.5590
INFO:causalml:   sMAPE (Treatment):     0.1410
INFO:causalml:    Gini   (Control):     0.7382
INFO:causalml:    Gini (Treatment):     0.9950
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0407
INFO:causalml:    RMSE (Treatment):     0.6737

{'X':            W4        W2        W1        W3        W0        X1        X0  \
54   1.459268  1.943608  1.129690  0.548664 -1.340973 -0.812905 -0.963531
785 -1.472432 -1.585313  0.723227 -2.198742 -0.861557  0.282930 -0.095028
295 -0.983868  1.058323 -1.353941  0.550563 -1.213079  0.165130  1.300355
411 -0.546985  0.632225  0.177670  1.152931 -1.341671 -1.043610  0.915882
854 -2.564260 -0.151503 -0.722982 -0.358408  0.118593 -0.081853  0.348541
..        ...       ...       ...       ...       ...       ...       ...
346 -1.786223  0.155241 -0.006492  1.116350  0.851448 -0.701068  0.717681
386  0.144990  2.200438  2.535124 -0.009489 -1.157723  1.477828 -0.341690
986 -0.416562  0.868225  0.665941  0.399819 -1.513787 -1.765043 -0.200477
428 -1.601123 -0.352920 -1.685513  2.750246 -2.723752  0.434913  0.292866
971 -1.011609  0.842579 -1.257641  1.094270  0.446977  1.836771  1.109406

           X0        X1
54  -0.963531 -0.812905
785 -0.095028  0.282930
295  1.300355  0.165130
411  0.915882 -1.043610
854  0.348541 -0.081853
..        ...       ...
346  0.717681 -0.701068
386 -0.341690  1.477828
986 -0.200477 -1.765043
428  0.292866  0.434913
971  1.109406  1.836771

[800 rows x 9 columns], 'y': 54      9.737109
785   -10.650231
295     9.933260
411     9.164325
854    -8.250436
         ...
346    10.270986
386    11.776341
986     3.757036
428     1.776248
971    16.046613
Name: y, Length: 800, dtype: float64, 'treatment': 54      True
785    False
295     True
411     True
854    False
       ...
346     True
386     True
986     True
428     True
971     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
905 -1.248790  0.836818  0.276427 -0.381741 -0.785530 -1.648800  0.638075
702  1.776446  1.312351  0.035714 -0.222481  1.693603  0.366529  0.704185
456 -1.180741  1.678642  0.789419  1.087730  0.037308  0.563234  0.500563
271 -1.683731 -0.490214  1.077274  1.927335 -0.447638  1.027982 -0.317068
191 -0.155299 -0.313327 -1.913569  0.091915 -1.423443  0.136986 -0.041962
..        ...       ...       ...       ...       ...       ...       ...
862 -2.626160 -0.085283  0.861203  0.598807  0.007442 -1.089904 -0.141363
499 -1.577195 -0.178122  0.207028  1.204303 -0.666172  1.145283  0.458618
373 -0.576617  0.832892 -0.252974  2.508469 -0.393241  0.636110  0.102158
932 -1.213129  0.643221 -0.007752  0.320722 -1.204954  0.065085 -0.764774
657 -1.103857  0.975798  0.409175  0.707782  0.346495 -0.957502  0.942159

           X0        X1
905  0.638075 -1.648800
702  0.704185  0.366529
456  0.500563  0.563234
271 -0.317068  1.027982
191 -0.041962  0.136986
..        ...       ...
862 -0.141363 -1.089904
499  0.458618  1.145283
373  0.102158  0.636110
932 -0.764774  0.065085
657  0.942159 -0.957502

[800 rows x 9 columns], 'y': 905    -5.143310
702    23.877017
456    12.752919
271     6.385709
191    -5.492128
         ...
862     1.585107
499     8.058945
373    12.268099
932     1.654133
657    12.117853
Name: y, Length: 800, dtype: float64, 'treatment': 905    False
702     True
456     True
271     True
191    False
       ...
862     True
499     True
373     True
932     True
657     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:   sMAPE   (Control):     0.5509
INFO:causalml:   sMAPE (Treatment):     0.1466
INFO:causalml:    Gini   (Control):     0.7399
INFO:causalml:    Gini (Treatment):     0.9956
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9439
INFO:causalml:    RMSE (Treatment):     0.7173
INFO:causalml:   sMAPE   (Control):     0.5251
INFO:causalml:   sMAPE (Treatment):     0.1418
INFO:causalml:    Gini   (Control):     0.7563
INFO:causalml:    Gini (Treatment):     0.9950
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
464 -1.458938  1.066085 -0.038658 -0.458790  0.563506  0.649619  0.007999
142 -1.069820 -0.045581  1.088194 -0.352007 -0.407053  1.435721  1.821025
216 -1.185130  1.723057 -2.436984 -0.947938 -0.797647 -1.237892  0.020300
227 -0.757316  2.009235 -0.832385  1.297304 -0.017571 -1.484499  0.073045
979 -1.320009 -0.547319  0.058828  1.048133  1.078373 -0.857583  0.480911
..        ...       ...       ...       ...       ...       ...       ...
172 -1.078399  1.722034 -1.675423 -0.026819 -0.577141 -1.633102  1.619526
964  0.753382  0.592423 -0.618551  0.045706 -0.493502 -1.581298 -1.715736
159 -1.948368 -0.614462  2.424841 -0.026382 -1.155858  0.788892  1.786834
171 -1.142720  1.363980 -1.309646 -0.806699 -1.397706  0.591713 -0.245067
499 -1.577195 -0.178122  0.207028  1.204303 -0.666172  1.145283  0.458618

           X0        X1
464  0.007999  0.649619
142  1.821025  1.435721
216  0.020300 -1.237892
227  0.073045 -1.484499
979  0.480911 -0.857583
..        ...       ...
172  1.619526 -1.633102
964 -1.715736 -1.581298
159  1.786834  0.788892
171 -0.245067  0.591713
499  0.458618  1.145283

[800 rows x 9 columns], 'y': 464     8.601975
142    14.255984
216     2.376140
227     9.555874
979    10.393456
         ...
172    10.160763
964     3.134159
159     9.334761
171     2.573635
499     8.058945
Name: y, Length: 800, dtype: float64, 'treatment': 464    True
142    True
216    True
227    True
979    True
       ...
172    True
964    True
159    True
171    True
499    True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0551
INFO:causalml:    RMSE (Treatment):     0.7052
INFO:causalml:   sMAPE   (Control):     0.5513
INFO:causalml:   sMAPE (Treatment):     0.1489
INFO:causalml:    Gini   (Control):     0.7201
INFO:causalml:    Gini (Treatment):     0.9948
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9918
INFO:causalml:    RMSE (Treatment):     0.7138
INFO:causalml:   sMAPE   (Control):     0.5188
INFO:causalml:   sMAPE (Treatment):     0.1520
INFO:causalml:    Gini   (Control):     0.7273
INFO:causalml:    Gini (Treatment):     0.9944
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9832
INFO:causalml:    RMSE (Treatment):     0.7111
INFO:causalml:   sMAPE   (Control):     0.5083
INFO:causalml:   sMAPE (Treatment):     0.1456
INFO:causalml:    Gini   (Control):     0.7489
INFO:causalml:    Gini (Treatment):     0.9949
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0359
INFO:causalml:    RMSE (Treatment):     0.6357

{'X':            W4        W2        W1        W3        W0        X1        X0  \
616 -1.830382  0.390144  1.344299  0.522921 -0.493492 -0.066279 -0.126978
441  0.096609  1.592914 -1.201277 -1.185385 -0.135004  0.210872  2.310466
743 -1.698782  0.965766  1.076097 -1.380195 -0.166558 -1.229501 -1.256471
565  0.489147  1.219821 -1.070019  0.305099  0.624808 -0.830622  1.045480
958 -0.495343  0.847053 -0.028871 -0.718100 -0.871586 -0.630299 -0.132512
..        ...       ...       ...       ...       ...       ...       ...
364 -1.078590  1.563574  1.504080 -0.486241 -1.540033 -0.435152  0.956055
965 -0.258222  2.196243 -1.509052  0.242610 -0.852991 -0.125788  3.020499
755 -0.785170 -0.634127  0.249551 -0.613209  0.804813 -1.015528  1.452350
7    0.174130  1.506210 -1.549595  0.782615 -1.878625 -0.626575  1.176085
137 -1.894324  0.875532 -1.274949 -0.005126 -1.668351 -0.299636 -1.162263

           X0        X1
616 -0.126978 -0.066279
441  2.310466  0.210872
743 -1.256471 -1.229501
565  1.045480 -0.830622
958 -0.132512 -0.630299
..        ...       ...
364  0.956055 -0.435152
965  3.020499 -0.125788
755  1.452350 -1.015528
7    1.176085 -0.626575
137 -1.162263 -0.299636

[800 rows x 9 columns], 'y': 616     4.604313
441    18.248395
743    -1.497744
565    17.014565
958     5.051010
         ...
364     7.693260
965    19.735711
755    12.541520
7      -2.646268
137    -9.707122
Name: y, Length: 800, dtype: float64, 'treatment': 616     True
441     True
743     True
565     True
958     True
       ...
364     True
965     True
755     True
7      False
137    False
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
671 -1.805567  1.966280  0.355705  0.675031 -1.531579 -0.217720 -0.701737
16  -0.022696  0.467212  0.670827 -0.516231 -0.682599  1.362676  1.401631
353 -0.079900  1.001506 -0.228440 -0.144542 -1.332903  0.275099  2.373142
480 -0.377755  0.312070  0.399832  1.532559  0.348411  0.225440  0.753623
452 -1.226268  1.393577  0.501651  1.386900 -0.728663 -2.554742  0.738239
..        ...       ...       ...       ...       ...       ...       ...
404  0.269959 -1.426995 -0.624877  1.207414 -0.632554  0.826927 -0.342909
339 -0.174635  1.102320 -0.538718  1.353400 -2.411045 -0.088519 -0.223676
538 -1.000985  2.417859  0.027563  1.436945  1.891137 -2.054885  0.763508
959 -1.282991  1.134722 -1.881201  0.830477  0.965414  1.414359 -1.140110
49  -0.982952  2.225363 -1.526931 -0.694241  0.029243 -0.455032  0.234971

           X0        X1
671 -0.701737 -0.217720
16   1.401631  1.362676
353  2.373142  0.275099
480  0.753623  0.225440
452  0.738239 -2.554742
..        ...       ...
404 -0.342909  0.826927
339 -0.223676 -0.088519
538  0.763508 -2.054885
959 -1.140110  1.414359
49   0.234971 -0.455032

[800 rows x 9 columns], 'y': 671    -6.150284
16     15.151640
353    16.031327
480    15.267030
452     7.577603
         ...
404    -1.323791
339     4.815990
538    16.761939
959     7.562645
49      8.274390
Name: y, Length: 800, dtype: float64, 'treatment': 671    False
16      True
353     True
480     True
452     True
       ...
404    False
339     True
538     True
959     True
49      True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
818 -2.294772  1.081263 -0.110948  0.240523 -2.452061 -0.676310  1.024324
441  0.096609  1.592914 -1.201277 -1.185385 -0.135004  0.210872  2.310466
660 -1.323851  1.441421  0.356298 -1.619747  1.211768 -0.134022  0.702311
907 -0.596246  0.342098  0.422666  1.205834 -0.189522 -0.157053  1.625369
150 -2.720297  0.875790 -0.364609 -0.201468  0.001957 -1.103898  0.006235
..        ...       ...       ...       ...       ...       ...       ...
569 -2.828271  0.496613  0.605941  1.991670  1.438382 -1.427814  2.464037
174 -1.209450  2.573548 -1.875041  0.466606 -2.593907 -0.787080  0.157327
618 -0.852827  2.817516  0.994045  0.632066  1.475903 -1.788458 -0.354627
578 -0.125015 -0.484719 -0.554158 -0.644919 -0.352951 -0.169403  0.807828
851 -1.580498  2.916707 -1.305058  0.877563  0.733961 -1.876975  2.623342

           X0        X1
818  1.024324 -0.676310
441  2.310466  0.210872
660  0.702311 -0.134022
907  1.625369 -0.157053
150  0.006235 -1.103898
..        ...       ...
569  2.464037 -1.427814
174  0.157327 -0.787080
618 -0.354627 -1.788458
578  0.807828 -0.169403
851  2.623342 -1.876975

[800 rows x 9 columns], 'y': 818   -11.770683
441    18.248395
660    11.491007
907    15.569400
150     1.212438
         ...
569    16.043709
174     1.619585
618    12.340446
578    -2.946371
851    18.155148
Name: y, Length: 800, dtype: float64, 'treatment': 818    False
441     True
660     True
907     True
150     True
       ...
569     True
174     True
618     True
578    False
851     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
14  -2.078772  1.196831  1.691383 -0.112157 -0.733879 -1.146159  0.723966
287 -2.059567 -0.680278 -2.183921  1.320290 -1.748679  0.879174  0.780425
429 -0.310856  1.184695  0.500596  1.413069 -1.423834 -2.391290 -0.004592
806 -0.731126  0.113323  0.240405  0.593963 -1.053335 -1.951187  2.046437
89  -0.956579  0.802858 -0.416669  0.542902 -0.031293  1.430714  1.345825
..        ...       ...       ...       ...       ...       ...       ...
546 -2.888766  0.186649  0.030494  0.288355 -1.192114 -0.335307  1.980589
279 -2.042622 -0.483061 -0.027763  0.310754  0.006903  0.717223  1.415660
732 -1.876595  1.781727 -0.587176  0.704493 -0.050272 -0.901322  2.047330
642 -1.263921  0.248475 -1.146400  0.651563 -0.523927  0.513736 -0.429372
614 -2.592181  1.103790 -0.570001  0.139735  0.205595 -0.448537 -0.211413

           X0        X1
14   0.723966 -1.146159
287  0.780425  0.879174
429 -0.004592 -2.391290
806  2.046437 -1.951187
89   1.345825  1.430714
..        ...       ...
546  1.980589 -0.335307
279  1.415660  0.717223
732  2.047330 -0.901322
642 -0.429372  0.513736
614 -0.211413 -0.448537

[800 rows x 9 columns], 'y': 14      5.340331
287   -11.017227
429     5.762485
806    11.154196
89     15.058027
         ...
546   -11.053337
279    -6.155145
732    -3.133831
642     4.413860
614    -5.992837
Name: y, Length: 800, dtype: float64, 'treatment': 14      True
287    False
429     True
806     True
89      True
       ...
546    False
279    False
732    False
642     True
614    False
Name: v0, Length: 800, dtype: bool}

INFO:causalml:   sMAPE   (Control):     0.5428
INFO:causalml:   sMAPE (Treatment):     0.1382
INFO:causalml:    Gini   (Control):     0.7706
INFO:causalml:    Gini (Treatment):     0.9965
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9307
INFO:causalml:    RMSE (Treatment):     0.6748
INFO:causalml:   sMAPE   (Control):     0.5415
INFO:causalml:   sMAPE (Treatment):     0.1462
INFO:causalml:    Gini   (Control):     0.7507
INFO:causalml:    Gini (Treatment):     0.9955
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0459
INFO:causalml:    RMSE (Treatment):     0.6579
INFO:causalml:   sMAPE   (Control):     0.5067
INFO:causalml:   sMAPE (Treatment):     0.1369
INFO:causalml:    Gini   (Control):     0.7317
INFO:causalml:    Gini (Treatment):     0.9958
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator

{'X':            W4        W2        W1        W3        W0        X1        X0  \
445 -0.093150  0.159054  1.219054  2.768834 -0.538133 -0.508483  1.145099
1   -0.612423  0.591947 -1.160895  1.779737 -1.083905  0.802709  0.713931
259  0.990156  0.812084 -0.696832 -1.386308 -1.233495  1.282252  1.415275
150 -2.720297  0.875790 -0.364609 -0.201468  0.001957 -1.103898  0.006235
845 -1.215958 -0.485289 -1.167214  0.914403 -1.773236  0.103620 -0.518889
..        ...       ...       ...       ...       ...       ...       ...
400 -0.340443 -1.712856  0.329522  1.261908 -0.358655  0.905733  1.694371
340  0.375616  2.217136 -0.666164 -0.970808  0.446217 -0.864206  1.763119
187  0.166212  1.756207  0.174379  0.739862 -0.309621  0.009743 -0.960418
53  -2.930083  0.879228 -0.579348  0.759465 -0.462352 -2.221874  2.337682
392  0.391364 -0.989100 -0.665144 -0.463719  1.025926  0.692766 -0.147856

           X0        X1
445  1.145099 -0.508483
1    0.713931  0.802709
259  1.415275  1.282252
150  0.006235 -1.103898
845 -0.518889  0.103620
..        ...       ...
400  1.694371  0.905733
340  1.763119 -0.864206
187 -0.960418  0.009743
53   2.337682 -2.221874
392 -0.147856  0.692766

[800 rows x 9 columns], 'y': 445    16.120442
1      11.125450
259    15.171532
150     1.212438
845    -8.309502
         ...
400    15.161858
340    18.529106
187     9.138120
53      7.558222
392    12.111471
Name: y, Length: 800, dtype: float64, 'treatment': 445     True
1       True
259     True
150     True
845    False
       ...
400     True
340     True
187     True
53      True
392     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
877 -0.359565  0.826991 -0.179359 -1.811258  1.418593  1.405163 -0.075609
948  0.137207  0.807432  0.012233 -0.321381 -1.727172 -0.265376  1.037937
906 -1.727409  0.768134  0.127817  2.012317 -0.001440  0.690172  0.662514
531 -1.304303 -0.065667 -1.069981  0.533144  1.279208 -0.691485 -0.259040
447 -0.405581  0.228388 -0.346772 -0.788431 -0.808578 -1.461212 -0.094927
..        ...       ...       ...       ...       ...       ...       ...
251 -2.083898 -0.553302  0.342455  1.195262 -0.140618 -0.475982  0.394482
329 -0.721478  0.206043  0.019891  0.334449 -2.015860  0.081232  2.050940
742 -1.894608  0.965814 -2.625684 -0.050702  0.089967 -0.260953  2.020572
291  0.257877 -0.247493 -0.219583  0.077716 -0.585046  0.209063  2.522633
984 -0.468952  1.419946 -1.223588 -0.200703  0.818225 -1.741452  0.553023

           X0        X1
877 -0.075609  1.405163
948  1.037937 -0.265376
906  0.662514  0.690172
531 -0.259040 -0.691485
447 -0.094927 -1.461212
..        ...       ...
251  0.394482 -0.475982
329  2.050940  0.081232
742  2.020572 -0.260953
291  2.522633  0.209063
984  0.553023 -1.741452

[800 rows x 9 columns], 'y': 877    12.698322
948     9.814477
906    11.554534
531     7.735845
447     3.661764
         ...
251     5.436045
329    10.777317
742    11.254116
291    18.348778
984    11.332827
Name: y, Length: 800, dtype: float64, 'treatment': 877    True
948    True
906    True
531    True
447    True
       ...
251    True
329    True
742    True
291    True
984    True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0274
INFO:causalml:    RMSE (Treatment):     0.6404
INFO:causalml:   sMAPE   (Control):     0.5697
INFO:causalml:   sMAPE (Treatment):     0.1380
INFO:causalml:    Gini   (Control):     0.7555
INFO:causalml:    Gini (Treatment):     0.9963
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1547
INFO:causalml:    RMSE (Treatment):     0.7120
INFO:causalml:   sMAPE   (Control):     0.5614
INFO:causalml:   sMAPE (Treatment):     0.1525
INFO:causalml:    Gini   (Control):     0.7153
INFO:causalml:    Gini (Treatment):     0.9952

{'X':            W4        W2        W1        W3        W0        X1        X0  \
333 -0.903943  1.303046 -0.373566  0.414038  0.837871 -0.085945  0.009761
600 -2.132836  0.973990 -0.800368  0.209837 -1.462109 -0.379117 -0.646683
296 -0.693534  1.091959  0.118506  1.534617 -1.801390  0.094255  1.938525
769 -1.913252  0.670484 -0.368978 -0.244239 -0.164518  0.285367  1.858510
877 -0.359565  0.826991 -0.179359 -1.811258  1.418593  1.405163 -0.075609
..        ...       ...       ...       ...       ...       ...       ...
463 -1.384868  1.213614 -1.227984  0.542554  0.851906  1.016157  0.823336
129 -0.761571 -0.442922 -1.576714  1.024327 -0.544396 -1.884830  0.425136
309 -0.539594  2.000693  1.298256  0.596046 -2.741050 -0.539698  0.178355
734 -2.534543  1.297053 -0.227526 -0.605377 -1.703765 -0.280342  0.415044
657 -1.103857  0.975798  0.409175  0.707782  0.346495 -0.957502  0.942159

           X0        X1
333  0.009761 -0.085945
600 -0.646683 -0.379117
296  1.938525  0.094255
769  1.858510  0.285367
877 -0.075609  1.405163
..        ...       ...
463  0.823336  1.016157
129  0.425136 -1.884830
309  0.178355 -0.539698
734  0.415044 -0.280342
657  0.942159 -0.957502

[800 rows x 9 columns], 'y': 333    11.223406
600    -2.038553
296    13.572697
769    -5.821681
877    12.698322
         ...
463    13.719677
129     5.487315
309     4.869483
734    -0.213830
657    12.117853
Name: y, Length: 800, dtype: float64, 'treatment': 333     True
600     True
296     True
769    False
877     True
       ...
463     True
129     True
309     True
734     True
657     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
866 -2.492060  0.007731 -0.940262 -0.496057  1.104068  0.043952  0.520345
426 -0.479176  1.237188 -0.726685 -0.314696  0.767152 -0.740509 -0.373064
930  0.236247  0.615868 -0.483059 -0.462038  0.084682  1.027238 -0.515047
649 -2.154564  1.280369 -0.834181  1.662134 -0.490512  0.051132  1.668837
41   0.075080 -0.116355 -0.810933  2.222729 -0.150383  0.082462 -0.018712
..        ...       ...       ...       ...       ...       ...       ...
401 -1.899841  1.517762 -1.564655  1.267017 -0.269945  0.348545  0.354959
90   0.097345  0.533868 -1.112249  0.999274  0.230117 -1.125698  0.717416
290  0.299643 -1.028151  0.230684 -0.517070  0.924262 -1.450635  0.759954
464 -1.458938  1.066085 -0.038658 -0.458790  0.563506  0.649619  0.007999
81  -2.075409 -0.271621 -0.306578  1.035155  0.414936 -0.005484  0.350309

           X0        X1
866  0.520345  0.043952
426 -0.373064 -0.740509
930 -0.515047  1.027238
649  1.668837  0.051132
41  -0.018712  0.082462
..        ...       ...
401  0.354959  0.348545
90   0.717416 -1.125698
290  0.759954 -1.450635
464  0.007999  0.649619
81   0.350309 -0.005484

[800 rows x 9 columns], 'y': 866     6.414822
426     8.947595
930    10.114187
649    11.515556
41     12.011428
         ...
401     7.798611
90     13.341932
290    12.664366
464     8.601975
81      7.063312
Name: y, Length: 800, dtype: float64, 'treatment': 866    True
426    True
930    True
649    True
41     True
       ...
401    True
90     True
290    True
464    True
81     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0349
INFO:causalml:    RMSE (Treatment):     0.7215
INFO:causalml:   sMAPE   (Control):     0.5344
INFO:causalml:   sMAPE (Treatment):     0.1396
INFO:causalml:    Gini   (Control):     0.7418
INFO:causalml:    Gini (Treatment):     0.9949
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0516
INFO:causalml:    RMSE (Treatment):     0.7011
INFO:causalml:   sMAPE   (Control):     0.5438
INFO:causalml:   sMAPE (Treatment):     0.1449
INFO:causalml:    Gini   (Control):     0.6956
INFO:causalml:    Gini (Treatment):     0.9943
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
311 -0.455229 -0.695322  0.209254 -0.763416 -1.934863 -0.037463  1.273690
120  0.925627  1.150181 -0.372738  1.184429 -1.147969 -0.032255  0.385113
429 -0.310856  1.184695  0.500596  1.413069 -1.423834 -2.391290 -0.004592
322  0.441476 -0.310950 -0.945903 -1.463723 -2.023630 -0.785600  1.276769
86  -1.171030  0.999708 -0.474575 -0.002353  1.214732 -0.432952 -1.419492
..        ...       ...       ...       ...       ...       ...       ...
848 -1.892578  1.453947 -2.287147  0.205164 -0.295408 -1.312249  0.506585
980 -1.323765  1.378697 -1.383845 -0.811301 -1.301810  0.635665  1.740498
569 -2.828271  0.496613  0.605941  1.991670  1.438382 -1.427814  2.464037
127 -1.184442  1.633850 -0.489806  0.806896 -0.654125 -0.324203 -1.297638
502 -1.817353  2.122041 -2.913312  1.536276 -0.699921 -0.900995  1.371631

           X0        X1
311  1.273690 -0.037463
120  0.385113 -0.032255
429 -0.004592 -2.391290
322  1.276769 -0.785600
86  -1.419492 -0.432952
..        ...       ...
848  0.506585 -1.312249
980  1.740498  0.635665
569  2.464037 -1.427814
127 -1.297638 -0.324203
502  1.371631 -0.900995

[800 rows x 9 columns], 'y': 311     6.548500
120    13.587443
429     5.762485
322     7.004125
86      4.893758
         ...
848    -5.747920
980     9.526360
569    16.043709
127     2.218793
502     9.319528
Name: y, Length: 800, dtype: float64, 'treatment': 311     True
120     True
429     True
322     True
86      True
       ...
848    False
980     True
569     True
127     True
502     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
572 -2.548463 -0.098732 -0.957317  0.639523 -0.249403  1.104659 -0.047994
221 -1.138293  0.865923 -1.177382 -0.509397 -2.476264  1.589415  0.625094
352 -0.314322 -0.439802  2.366604  0.701073 -0.609823  0.603549 -0.256803
850 -1.559250  1.506724  0.449602  0.956745 -1.983230 -0.410145  2.749812
582 -1.856578  1.519025 -0.354817  1.777607 -0.103456 -0.540906  0.894554
..        ...       ...       ...       ...       ...       ...       ...
980 -1.323765  1.378697 -1.383845 -0.811301 -1.301810  0.635665  1.740498
811 -1.803447  0.510549 -1.668762  1.850597 -0.358186 -2.226574  2.080508
789 -1.419550 -0.199775  1.439845 -0.043468 -0.070078  0.347234  1.237806
883 -1.053935 -0.020818 -0.218070 -0.163484 -1.436398  0.888213  0.560956
164 -1.968714  0.633982 -0.864566  0.029554 -0.968736 -1.092772  0.316363

           X0        X1
572 -0.047994  1.104659
221  0.625094  1.589415
352 -0.256803  0.603549
850  2.749812 -0.410145
582  0.894554 -0.540906
..        ...       ...
980  1.740498  0.635665
811  2.080508 -2.226574
789  1.237806  0.347234
883  0.560956  0.888213
164  0.316363 -1.092772

[800 rows x 9 columns], 'y': 572    -8.024569
221   -10.175373
352     8.970110
850    12.795708
582    10.549091
         ...
980     9.526360
811    10.541175
789    11.103887
883     5.901248
164     1.757613
Name: y, Length: 800, dtype: float64, 'treatment': 572    False
221    False
352     True
850     True
582     True
       ...
980     True
811     True
789     True
883     True
164     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0202
INFO:causalml:    RMSE (Treatment):     0.6791
INFO:causalml:   sMAPE   (Control):     0.5553
INFO:causalml:   sMAPE (Treatment):     0.1368
INFO:causalml:    Gini   (Control):     0.7275
INFO:causalml:    Gini (Treatment):     0.9955
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0764
INFO:causalml:    RMSE (Treatment):     0.7291
INFO:causalml:   sMAPE   (Control):     0.5471
INFO:causalml:   sMAPE (Treatment):     0.1549
INFO:causalml:    Gini   (Control):     0.7327
INFO:causalml:    Gini (Treatment):     0.9950
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
720 -0.178107  0.545358 -1.188979 -2.009287 -1.712708  0.183073  2.293672
794 -1.233396 -0.435764 -0.476026  0.431914  0.233094 -1.611720  0.182139
964  0.753382  0.592423 -0.618551  0.045706 -0.493502 -1.581298 -1.715736
738 -0.532602  1.680468 -1.570149  2.071328 -0.883423 -2.507779  0.510461
813 -2.510734  1.799308 -1.388065  0.395407  0.345451 -1.133800  0.417955
..        ...       ...       ...       ...       ...       ...       ...
386  0.144990  2.200438  2.535124 -0.009489 -1.157723  1.477828 -0.341690
975 -0.891387  0.694594  0.240509  1.789194 -0.619145 -1.193071  0.675476
731 -0.792085  2.126007 -1.140046  0.187479 -0.140220 -0.338651  1.396477
807  0.395378  2.501552  0.964778  0.986126 -0.742713 -1.556838 -0.017120
419 -0.220525 -0.582744  0.201690  0.269966 -0.821470  1.089105  1.028671

           X0        X1
720  2.293672  0.183073
794  0.182139 -1.611720
964 -1.715736 -1.581298
738  0.510461 -2.507779
813  0.417955 -1.133800
..        ...       ...
386 -0.341690  1.477828
975  0.675476 -1.193071
731  1.396477 -0.338651
807 -0.017120 -1.556838
419  1.028671  1.089105

[800 rows x 9 columns], 'y': 720    11.018665
794     5.470791
964     3.134159
738     8.470271
813     5.390940
         ...
386    11.776341
975     9.860888
731    13.912472
807    11.784838
419    12.060932
Name: y, Length: 800, dtype: float64, 'treatment': 720    True
794    True
964    True
738    True
813    True
       ...
386    True
975    True
731    True
807    True
419    True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
395 -1.795386  0.484380 -0.308798  0.356361  0.933391 -1.198681  0.592933
991 -0.283700  0.699498  0.106398 -1.059915 -2.411506 -0.204126  1.891908
336 -3.093855  1.469971  0.367279  1.174817 -0.729766 -2.114001  0.885400
344 -2.504980  1.383036 -0.141410 -0.360349 -1.132968 -0.013613  1.887884
773 -1.096836 -0.432951 -0.837524 -0.231672 -0.535628 -1.020083 -0.353180
..        ...       ...       ...       ...       ...       ...       ...
979 -1.320009 -0.547319  0.058828  1.048133  1.078373 -0.857583  0.480911
131 -0.636926  1.412123 -1.568989  1.128580 -1.107298 -0.356463  3.129605
409 -0.542454  0.428394  1.357376  0.037163 -0.476170  1.308313  2.058995
807  0.395378  2.501552  0.964778  0.986126 -0.742713 -1.556838 -0.017120
594 -0.485088  0.198923  0.215890  1.387663  0.491391 -1.501354 -0.527038

           X0        X1
395  0.592933 -1.198681
991  1.891908 -0.204126
336  0.885400 -2.114001
344  1.887884 -0.013613
773 -0.353180 -1.020083
..        ...       ...
979  0.480911 -0.857583
131  3.129605 -0.356463
409  2.058995  1.308313
807 -0.017120 -1.556838
594 -0.527038 -1.501354

[800 rows x 9 columns], 'y': 395     8.681410
991    -7.648859
336     2.926169
344    -9.322258
773     1.630931
         ...
979    10.393456
131    18.255680
409    17.513846
807    11.784838
594     8.167200
Name: y, Length: 800, dtype: float64, 'treatment': 395     True
991    False
336     True
344    False
773     True
       ...
979     True
131     True
409     True
807     True
594     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0838
INFO:causalml:    RMSE (Treatment):     0.7578
INFO:causalml:   sMAPE   (Control):     0.5357
INFO:causalml:   sMAPE (Treatment):     0.1493
INFO:causalml:    Gini   (Control):     0.7243
INFO:causalml:    Gini (Treatment):     0.9939
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0498
INFO:causalml:    RMSE (Treatment):     0.7149
INFO:causalml:   sMAPE   (Control):     0.5699
INFO:causalml:   sMAPE (Treatment):     0.1427
INFO:causalml:    Gini   (Control):     0.7412
INFO:causalml:    Gini (Treatment):     0.9951
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0743
INFO:causalml:    RMSE (Treatment):     0.7071
INFO:causalml:   sMAPE   (Control):     0.5430
INFO:causalml:   sMAPE (Treatment):     0.1417
INFO:causalml:    Gini   (Control):     0.7392
INFO:causalml:    Gini (Treatment):     0.9952

{'X':            W4        W2        W1        W3        W0        X1        X0  \
893 -1.491824  0.452183 -1.205184  0.340149  1.739224 -0.632809  0.210046
456 -1.180741  1.678642  0.789419  1.087730  0.037308  0.563234  0.500563
368 -2.298170  1.146523 -1.218792 -0.454870 -0.475421 -1.530454  0.327366
558 -0.550544  0.440434 -0.750181  1.612420  0.551244  0.168593  1.698572
287 -2.059567 -0.680278 -2.183921  1.320290 -1.748679  0.879174  0.780425
..        ...       ...       ...       ...       ...       ...       ...
935 -1.612218  0.425603  0.450798  1.795192 -1.030650  0.071948  0.533019
148 -0.302752 -0.391139 -1.411531 -0.984236 -1.414685 -1.430286  1.022603
628 -0.907745  0.221924 -0.912103  0.680653 -0.919082  0.488610 -0.844236
356 -1.087973  1.063452 -0.582690 -0.391019 -0.717769  0.567377  0.968256
39  -0.961481  1.275170 -1.485334 -0.763943 -1.116911  0.131391  1.712740

           X0        X1
893  0.210046 -0.632809
456  0.500563  0.563234
368  0.327366 -1.530454
558  1.698572  0.168593
287  0.780425  0.879174
..        ...       ...
935  0.533019  0.071948
148  1.022603 -1.430286
628 -0.844236  0.488610
356  0.968256  0.567377
39   1.712740  0.131391

[800 rows x 9 columns], 'y': 893    10.444157
456    12.752919
368     1.367327
558    18.249687
287   -11.017227
         ...
935     7.518694
148     4.946877
628    -4.505402
356    -4.713297
39     -6.109185
Name: y, Length: 800, dtype: float64, 'treatment': 893     True
456     True
368     True
558     True
287    False
       ...
935     True
148     True
628    False
356    False
39     False
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
900 -1.528877  0.854363 -0.020892 -0.125090  1.283825  0.408115 -0.168281
363 -1.299538  1.492361 -0.176621 -0.788150 -0.271734 -2.617038  1.663896
692 -1.425142 -0.090768 -2.394217  0.281658 -1.069567 -1.236345 -1.514344
226 -1.070717  0.609382 -3.049646  1.477891 -0.338049  0.433746  0.098532
856 -2.193329  0.959709 -2.021881 -0.809934 -2.201305 -2.098867 -0.278113
..        ...       ...       ...       ...       ...       ...       ...
98  -0.724707 -0.167508 -2.200046  1.316365 -0.652669  1.877820  0.372580
531 -1.304303 -0.065667 -1.069981  0.533144  1.279208 -0.691485 -0.259040
607 -0.713550 -0.806284 -0.339457 -0.279553 -0.477920 -0.690686 -0.221325
522  0.488747  0.260763 -0.084712  2.012467  0.136177 -0.514404  1.454348
813 -2.510734  1.799308 -1.388065  0.395407  0.345451 -1.133800  0.417955

           X0        X1
900 -0.168281  0.408115
363  1.663896 -2.617038
692 -1.514344 -1.236345
226  0.098532  0.433746
856 -0.278113 -2.098867
..        ...       ...
98   0.372580  1.877820
531 -0.259040 -0.691485
607 -0.221325 -0.690686
522  1.454348 -0.514404
813  0.417955 -1.133800

[800 rows x 9 columns], 'y': 900     9.560536
363     8.937908
692    -8.079772
226    -3.230951
856   -13.300039
         ...
98     -3.658929
531     7.735845
607     3.571733
522    19.196033
813     5.390940
Name: y, Length: 800, dtype: float64, 'treatment': 900     True
363     True
692    False
226    False
856    False
       ...
98     False
531     True
607     True
522     True
813     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
695 -0.197821 -0.512162  0.067361  0.935517 -0.381064 -1.154606  0.317349
486 -0.131758 -0.288235 -0.600001  0.396915  0.962738 -1.026124  0.537855
43  -0.954425  1.158370  0.086873  0.568439 -0.884157 -0.410623  1.426287
418 -1.575082 -0.671851 -0.031660 -0.315301  0.096593  0.101204 -0.833223
122 -2.951921 -0.276981 -1.228503  0.841593  0.132569 -1.710101  0.432787
..        ...       ...       ...       ...       ...       ...       ...
142 -1.069820 -0.045581  1.088194 -0.352007 -0.407053  1.435721  1.821025
667 -2.198198  0.869380  0.049001  1.338380 -0.046996  0.313350  1.524353
560 -0.591932  0.047587 -1.357618 -1.008455 -1.341175  0.311592  1.261070
355 -0.326494  2.433717  0.080853  1.915810 -0.164674  0.466589 -0.152236
738 -0.532602  1.680468 -1.570149  2.071328 -0.883423 -2.507779  0.510461

           X0        X1
695  0.317349 -1.154606
486  0.537855 -1.026124
43   1.426287 -0.410623
418 -0.833223  0.101204
122  0.432787 -1.710101
..        ...       ...
142  1.821025  1.435721
667  1.524353  0.313350
560  1.261070  0.311592
355 -0.152236  0.466589
738  0.510461 -2.507779

[800 rows x 9 columns], 'y': 695     8.766754
486    12.711964
43     11.590880
418    -5.534462
122    -8.281570
         ...
142    14.255984
667    11.985876
560    -7.215301
355    13.712619
738     8.470271
Name: y, Length: 800, dtype: float64, 'treatment': 695     True
486     True
43      True
418    False
122    False
       ...
142     True
667     True
560    False
355     True
738     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0525
INFO:causalml:    RMSE (Treatment):     0.7187
INFO:causalml:   sMAPE   (Control):     0.5312
INFO:causalml:   sMAPE (Treatment):     0.1296
INFO:causalml:    Gini   (Control):     0.7540
INFO:causalml:    Gini (Treatment):     0.9948
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1200
INFO:causalml:    RMSE (Treatment):     0.7349

{'X':            W4        W2        W1        W3        W0        X1        X0  \
640 -1.678246  1.438381 -0.155114  1.449979 -1.577508 -0.659106  0.118324
827 -0.123357 -0.188634  0.382711 -0.500811  1.739447  0.613970  0.748232
673 -0.941288 -0.348886 -1.817197 -0.426040 -0.139103 -0.649783  0.857110
284 -0.929719  0.896653 -0.016749 -0.051014 -2.466214 -0.577574  0.515098
295 -0.983868  1.058323 -1.353941  0.550563 -1.213079  0.165130  1.300355
..        ...       ...       ...       ...       ...       ...       ...
178 -1.287866  0.105602  0.132745 -0.181920 -0.151254 -1.038557  0.408551
723 -0.107996  1.393754  0.825508  0.735281 -0.586520 -0.348836  0.538970
394 -1.283395  1.760831 -0.728146  0.613214  0.534818  0.482541  0.497303
831 -1.230133 -0.572774 -0.868328  0.372532 -2.065409 -1.925431  1.802943
651 -0.647903 -0.439082 -1.170327 -0.407629  1.252680 -0.108644  0.227973

           X0        X1
640  0.118324 -0.659106
827  0.748232  0.613970
673  0.857110 -0.649783
284  0.515098 -0.577574
295  1.300355  0.165130
..        ...       ...
178  0.408551 -1.038557
723  0.538970 -0.348836
394  0.497303  0.482541
831  1.802943 -1.925431
651  0.227973 -0.108644

[800 rows x 9 columns], 'y': 640     3.839554
827    17.063845
673    -5.059295
284     2.864562
295     9.933260
         ...
178     6.014561
723    12.592456
394    12.331256
831     4.497600
651    10.451031
Name: y, Length: 800, dtype: float64, 'treatment': 640     True
827     True
673    False
284     True
295     True
       ...
178     True
723     True
394     True
831     True
651     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
235 -0.263945  0.606153 -2.132472  0.508581 -0.376937  0.208302 -1.639252
293 -2.019915 -0.918739 -1.767926 -1.403425 -0.740373 -1.576824  0.279021
505 -2.479285  0.847818  0.513598 -0.218587  0.793758 -1.587561  1.340198
816 -0.262987  0.921280 -0.389069 -0.883456 -2.158824 -1.767588 -0.312548
877 -0.359565  0.826991 -0.179359 -1.811258  1.418593  1.405163 -0.075609
..        ...       ...       ...       ...       ...       ...       ...
677 -0.926078  0.166307 -2.183101  1.988610 -0.286812 -1.952046  0.131363
925 -0.292250 -0.487899 -1.791678  1.743438 -1.451384 -1.451548 -0.034151
455 -0.472574  1.272672 -2.489234  0.483832  0.334740 -0.703027  1.687994
457 -1.376599  1.461089 -0.718660  1.781923 -2.802867 -1.477809  0.128276
665 -2.174730  0.248624 -0.092817 -0.715735  0.210519 -0.742166 -0.077510

           X0        X1
235 -1.639252  0.208302
293  0.279021 -1.576824
505  1.340198 -1.587561
816 -0.312548 -1.767588
877 -0.075609  1.405163
..        ...       ...
677  0.131363 -1.952046
925 -0.034151 -1.451548
455  1.687994 -0.703027
457  0.128276 -1.477809
665 -0.077510 -0.742166

[800 rows x 9 columns], 'y': 235     2.634861
293   -11.609205
505     8.711479
816    -0.020121
877    12.698322
         ...
677     6.044637
925     4.053751
455    15.390761
457    -8.028104
665     2.331887
Name: y, Length: 800, dtype: float64, 'treatment': 235     True
293    False
505     True
816     True
877     True
       ...
677     True
925     True
455     True
457    False
665     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:   sMAPE   (Control):     0.5751
INFO:causalml:   sMAPE (Treatment):     0.1464
INFO:causalml:    Gini   (Control):     0.7218
INFO:causalml:    Gini (Treatment):     0.9944
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0223
INFO:causalml:    RMSE (Treatment):     0.7354
INFO:causalml:   sMAPE   (Control):     0.5407
INFO:causalml:   sMAPE (Treatment):     0.1396
INFO:causalml:    Gini   (Control):     0.7417
INFO:causalml:    Gini (Treatment):     0.9942
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0009
INFO:causalml:    RMSE (Treatment):     0.7661
INFO:causalml:   sMAPE   (Control):     0.5483
INFO:causalml:   sMAPE (Treatment):     0.1548
INFO:causalml:    Gini   (Control):     0.7414
INFO:causalml:    Gini (Treatment):     0.9938
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
831 -1.230133 -0.572774 -0.868328  0.372532 -2.065409 -1.925431  1.802943
872 -0.675589  1.480966  0.633872 -1.812491  0.279114 -0.021202  0.937137
895 -0.459769 -0.017087 -0.860174  0.294297  0.061199  0.406711  0.845962
102 -0.418300 -0.557531 -1.665453  1.690287  0.196389 -1.122764  0.983299
686 -1.377233 -0.617476 -0.353271  2.000711 -1.831548  0.632213  0.262429
..        ...       ...       ...       ...       ...       ...       ...
412 -1.218461  2.898433 -0.737451 -1.217685  0.214579 -1.178889  0.280958
908 -0.171439  0.987004  1.139063 -0.049279 -1.404765 -0.687979 -0.500347
340  0.375616  2.217136 -0.666164 -0.970808  0.446217 -0.864206  1.763119
117 -2.185549  0.899307 -1.971894 -1.174155 -1.218568 -0.519142  1.033019
772 -1.631390  0.465915 -1.149010  1.590925 -0.376748 -1.413846 -0.295266

           X0        X1
831  1.802943 -1.925431
872  0.937137 -0.021202
895  0.845962  0.406711
102  0.983299 -1.122764
686  0.262429  0.632213
..        ...       ...
412  0.280958 -1.178889
908 -0.500347 -0.687979
340  1.763119 -0.864206
117  1.033019 -0.519142
772 -0.295266 -1.413846

[800 rows x 9 columns], 'y': 831     4.497600
872    11.901044
895    12.243751
102    12.024923
686     4.432389
         ...
412     7.872612
908     4.820482
340    18.529106
117   -11.180244
772     3.250030
Name: y, Length: 800, dtype: float64, 'treatment': 831     True
872     True
895     True
102     True
686     True
       ...
412     True
908     True
340     True
117    False
772     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
550 -0.456966 -0.476118  0.221099 -0.054011  0.708884 -1.962707  0.617111
776 -2.842646 -0.725526 -0.406744 -0.025141 -1.114379  0.538557  1.710464
208 -1.829170  0.787526 -0.881231  0.083780 -1.917817 -2.457417  0.754683
162 -1.388900  0.882150 -0.922345  0.504856  1.804151 -2.140192  2.410467
904 -1.109443  3.107200 -0.951024 -0.079768  1.948789 -1.132518 -0.890302
..        ...       ...       ...       ...       ...       ...       ...
184 -1.891342  1.936913 -1.618877 -1.053819  0.021617 -0.044179 -0.895357
960  0.148135 -0.231778  0.068080 -1.110248 -3.242602 -0.436255  1.334302
281 -1.135111  0.940750 -0.067170 -0.759354  1.096828 -1.630748  0.625826
84  -1.231904  0.946563 -0.520574 -1.241545 -1.073605  0.279123  0.201559
608  0.655518  2.888275 -1.833586 -1.459779  0.528316  1.313325 -0.562842

           X0        X1
550  0.617111 -1.962707
776  1.710464  0.538557
208  0.754683 -2.457417
162  2.410467 -2.140192
904 -0.890302 -1.132518
..        ...       ...
184 -0.895357 -0.044179
960  1.334302 -0.436255
281  0.625826 -1.630748
84   0.201559  0.279123
608 -0.562842  1.313325

[800 rows x 9 columns], 'y': 550     9.910321
776   -12.340879
208    -9.928296
162    17.934588
904    10.065464
         ...
184     1.132702
960     4.643805
281     9.917053
84     -7.210816
608    13.248344
Name: y, Length: 800, dtype: float64, 'treatment': 550     True
776    False
208    False
162     True
904     True
       ...
184     True
960     True
281     True
84     False
608     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
981 -1.677948 -1.622248 -1.171652  0.349836 -2.213015  1.101661  0.722546
252 -0.407608 -0.441935 -1.985984  1.444090  0.083796 -0.670731  0.259822
433 -1.782608  1.831915 -0.845111  0.759755  0.586684 -1.953787  0.353037
190 -1.284989  0.161720 -0.195371  0.142880 -1.766458 -1.817200  1.122913
380 -1.222122 -0.625482 -0.290597  0.559950 -0.992344 -0.491524 -0.854497
..        ...       ...       ...       ...       ...       ...       ...
383 -1.820459  0.011031 -1.003686 -0.301110  0.254573 -1.724935  0.863482
875 -2.411078  0.159398  0.119580  0.748787 -0.457547  0.313736 -0.560625
204 -1.495909  1.711115 -1.457953  0.493295  1.141122  0.318999  2.147456
635 -0.309105  1.995839 -1.442744  1.469303 -0.866598  1.284870  1.107725
18  -1.721390  0.991295 -1.008932  1.539819 -0.923055 -0.609885 -0.196033

           X0        X1
981  0.722546  1.101661
252  0.259822 -0.670731
433  0.353037 -1.953787
190  1.122913 -1.817200
380 -0.854497 -0.491524
..        ...       ...
383  0.863482 -1.724935
875 -0.560625  0.313736
204  2.147456  0.318999
635  1.107725  1.284870
18  -0.196033 -0.609885

[800 rows x 9 columns], 'y': 981   -12.765797
252    -0.786820
433     7.710126
190     3.713965
380    -0.053237
         ...
383     5.450868
875     1.205333
204    18.498160
635    15.613398
18     -5.046192
Name: y, Length: 800, dtype: float64, 'treatment': 981    False
252    False
433     True
190     True
380     True
       ...
383     True
875     True
204     True
635     True
18     False
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1101
INFO:causalml:    RMSE (Treatment):     0.6556
INFO:causalml:   sMAPE   (Control):     0.5635
INFO:causalml:   sMAPE (Treatment):     0.1410
INFO:causalml:    Gini   (Control):     0.7584
INFO:causalml:    Gini (Treatment):     0.9964
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0568
INFO:causalml:    RMSE (Treatment):     0.7481
INFO:causalml:   sMAPE   (Control):     0.5376
INFO:causalml:   sMAPE (Treatment):     0.1475
INFO:causalml:    Gini   (Control):     0.7403
INFO:causalml:    Gini (Treatment):     0.9942
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0602
INFO:causalml:    RMSE (Treatment):     0.7217
INFO:causalml:   sMAPE   (Control):     0.5135
INFO:causalml:   sMAPE (Treatment):     0.1480

{'X':            W4        W2        W1        W3        W0        X1        X0  \
455 -0.472574  1.272672 -2.489234  0.483832  0.334740 -0.703027  1.687994
576 -0.258472  1.109831  0.044998  0.470214  0.134336 -1.293955  1.695302
236 -0.724433  0.998208  0.238311 -1.291449 -1.941921  0.913822  0.345125
687  0.178787  0.595348 -1.587125  1.513139 -0.245915 -0.379845  2.377876
192 -0.062807  0.029133 -0.344053  0.098793 -0.395855 -0.545119  0.774679
..        ...       ...       ...       ...       ...       ...       ...
65  -2.860295  0.491337  0.307032  0.402665  0.265118 -1.899636 -0.565288
983 -1.330248  1.247409 -0.571754 -0.359552  0.349860 -0.123228 -0.031874
47   0.154544  1.025493 -1.250332 -1.536762 -0.309219 -0.948781  1.324595
269  0.573973 -0.053532 -0.674589 -3.004365  1.021180 -0.146757  0.753673
422 -1.830252  0.894077 -0.267716 -0.367326  2.202751 -0.079619  0.119763

           X0        X1
455  1.687994 -0.703027
576  1.695302 -1.293955
236  0.345125  0.913822
687  2.377876 -0.379845
192  0.774679 -0.545119
..        ...       ...
65  -0.565288 -1.899636
983 -0.031874 -0.123228
47   1.324595 -0.948781
269  0.753673 -0.146757
422  0.119763 -0.079619

[800 rows x 9 columns], 'y': 455    15.390761
576    16.021321
236     4.765758
687    19.701253
192    10.887872
         ...
65     -0.860515
983     7.361146
47     11.849394
269     0.249321
422    11.141529
Name: y, Length: 800, dtype: float64, 'treatment': 455     True
576     True
236     True
687     True
192     True
       ...
65      True
983     True
47      True
269    False
422     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
143 -2.040739  1.654124  2.203868  1.326991 -0.370663 -1.606991 -0.643100
627 -1.523690 -0.192391  1.149614 -0.762336 -0.680800 -0.533576 -0.097514
890 -1.222633  2.135828 -0.734609 -0.488569 -0.600416 -0.479990 -0.460961
378 -1.938741  0.891352  0.002345  1.742060 -0.104084 -0.598398  0.006701
451 -0.565758  1.128663 -0.324990 -0.337899 -0.015063  0.346414  0.109342
..        ...       ...       ...       ...       ...       ...       ...
513 -0.719556  1.594492 -0.676926  2.386985  3.196747 -0.287512  0.591108
111  1.024268  0.556637 -0.514663  1.858435 -0.414872 -0.567619  0.793652
460 -0.386371  1.170936  0.655040  0.226403 -0.871820 -0.967792  1.052236
238 -0.166010  0.774921 -0.462897 -0.294769 -1.389314 -0.210174  2.431329
206  0.034341 -1.189055 -0.976649  0.149847  1.575362 -0.130203  2.269800

           X0        X1
143 -0.643100 -1.606991
627 -0.097514 -0.533576
890 -0.460961 -0.479990
378  0.006701 -0.598398
451  0.109342  0.346414
..        ...       ...
513  0.591108 -0.287512
111  0.793652 -0.567619
460  1.052236 -0.967792
238  2.431329 -0.210174
206  2.269800 -0.130203

[800 rows x 9 columns], 'y': 143     3.430961
627     2.219546
890    -3.809211
378    -2.856631
451     9.760052
         ...
513    22.411994
111    16.735009
460    11.172108
238    14.670040
206    20.680223
Name: y, Length: 800, dtype: float64, 'treatment': 143     True
627     True
890    False
378    False
451     True
       ...
513     True
111     True
460     True
238     True
206     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    Gini   (Control):     0.7365
INFO:causalml:    Gini (Treatment):     0.9949
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0490
INFO:causalml:    RMSE (Treatment):     0.6879
INFO:causalml:   sMAPE   (Control):     0.5267
INFO:causalml:   sMAPE (Treatment):     0.1384
INFO:causalml:    Gini   (Control):     0.7423
INFO:causalml:    Gini (Treatment):     0.9953
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9082
INFO:causalml:    RMSE (Treatment):     0.6356
INFO:causalml:   sMAPE   (Control):     0.5205
INFO:causalml:   sMAPE (Treatment):     0.1334
INFO:causalml:    Gini   (Control):     0.7233
INFO:causalml:    Gini (Treatment):     0.9956
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
808 -0.291951  1.043771 -0.571882 -0.537580  0.895934 -1.203433  1.200730
359 -1.250892  0.608337 -1.159236  1.196291 -2.448155 -1.418204  2.171817
755 -0.785170 -0.634127  0.249551 -0.613209  0.804813 -1.015528  1.452350
656 -1.273620  1.797036  2.092367  0.953316  1.197489 -0.625639  0.731893
464 -1.458938  1.066085 -0.038658 -0.458790  0.563506  0.649619  0.007999
..        ...       ...       ...       ...       ...       ...       ...
251 -2.083898 -0.553302  0.342455  1.195262 -0.140618 -0.475982  0.394482
8   -1.192098  0.543069  0.448059  1.496816  1.148883 -0.101334  0.352319
573  0.196341  0.991673 -0.825979  1.860407 -1.870355 -1.152153 -1.051394
726 -0.283535  0.621638 -0.999520  0.843142  1.790317 -0.030857  0.902776
192 -0.062807  0.029133 -0.344053  0.098793 -0.395855 -0.545119  0.774679

           X0        X1
808  1.200730 -1.203433
359  2.171817 -1.418204
755  1.452350 -1.015528
656  0.731893 -0.625639
464  0.007999  0.649619
..        ...       ...
251  0.394482 -0.475982
8    0.352319 -0.101334
573 -1.051394 -1.152153
726  0.902776 -0.030857
192  0.774679 -0.545119

[800 rows x 9 columns], 'y': 808    14.588358
359     7.556557
755    12.541520
656    15.632059
464     8.601975
         ...
251     5.436045
8      13.374297
573     3.364299
726    18.301691
192    10.887872
Name: y, Length: 800, dtype: float64, 'treatment': 808    True
359    True
755    True
656    True
464    True
       ...
251    True
8      True
573    True
726    True
192    True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
121  1.534365  1.840154 -0.218035 -0.257249 -0.734629 -0.767329 -1.295119
589 -1.667998 -0.312881 -0.874289 -0.432761 -1.519063  0.481366 -0.232169
524  0.057014  0.649900 -0.364445 -0.578270  0.198899 -0.364881  2.339120
390 -2.802559  0.829055 -0.457778  0.755228  0.148033 -0.859578  0.281249
933 -0.934060  1.319778  0.959669 -0.733035 -1.291453 -0.773287  0.716901
..        ...       ...       ...       ...       ...       ...       ...
415 -0.927855  0.937062  0.139474 -0.230930 -1.822390 -1.141240  3.168139
351 -0.741034  0.497864 -0.483110  1.193559 -0.155819 -1.606313  0.005075
967 -1.386450  1.648145 -0.953457  1.161783 -0.494481 -3.161705  1.259562
403 -1.630763  2.405041  0.712370  0.529186 -2.230960 -0.416914 -1.593093
966 -2.809966  2.310766  0.855738  2.265568 -1.872551 -0.553294  2.176341

           X0        X1
121 -1.295119 -0.767329
589 -0.232169  0.481366
524  2.339120 -0.364881
390  0.281249 -0.859578
933  0.716901 -0.773287
..        ...       ...
415  3.168139 -1.141240
351  0.005075 -1.606313
967  1.259562 -3.161705
403 -1.593093 -0.416914
966  2.176341 -0.553294

[800 rows x 9 columns], 'y': 121     8.541463
589   -10.258505
524    18.575304
390     3.725711
933     6.639250
         ...
415    -6.705046
351     7.214058
967    -2.657495
403    -3.265557
966     9.876889
Name: y, Length: 800, dtype: float64, 'treatment': 121     True
589    False
524     True
390     True
933     True
       ...
415    False
351     True
967    False
403     True
966     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
190 -1.284989  0.161720 -0.195371  0.142880 -1.766458 -1.817200  1.122913
314 -0.846455 -0.409130 -2.466592  0.522989 -0.052613 -2.655562  1.377086
42   0.672800  0.403843 -0.485776  1.240526  1.063470  0.870394  0.561993
242 -1.740568  1.485447 -2.312406  0.386711  1.155020 -1.148651 -1.499194
268 -0.008387 -0.345640  0.297517  0.932659 -0.233741 -0.957095 -1.559348
..        ...       ...       ...       ...       ...       ...       ...
731 -0.792085  2.126007 -1.140046  0.187479 -0.140220 -0.338651  1.396477
689 -2.243570  1.275778 -1.424485 -2.440678 -1.906424 -2.373430 -0.198568
366 -1.403735  1.313386  1.988391 -0.578266  1.442075  1.501325  0.150474
179 -1.297348  2.058718 -1.459938  0.264849  0.264363 -1.481147  1.305092
237 -0.843070  3.028032 -0.964293  0.319940 -2.199262 -0.535469 -1.279896

           X0        X1
190  1.122913 -1.817200
314  1.377086 -2.655562
42   0.561993  0.870394
242 -1.499194 -1.148651
268 -1.559348 -0.957095
..        ...       ...
731  1.396477 -0.338651
689 -0.198568 -2.373430
366  0.150474  1.501325
179  1.305092 -1.481147
237 -1.279896 -0.535469

[800 rows x 9 columns], 'y': 190     3.713965
314    -3.811119
42     19.574715
242     1.894728
268     3.465358
         ...
731    13.912472
689   -13.994008
366    13.879918
179    11.624002
237    -0.336643
Name: y, Length: 800, dtype: float64, 'treatment': 190     True
314    False
42      True
242     True
268     True
       ...
731     True
689    False
366     True
179     True
237     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9942
INFO:causalml:    RMSE (Treatment):     0.7756
INFO:causalml:   sMAPE   (Control):     0.5198
INFO:causalml:   sMAPE (Treatment):     0.1502
INFO:causalml:    Gini   (Control):     0.7338
INFO:causalml:    Gini (Treatment):     0.9935
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9713
INFO:causalml:    RMSE (Treatment):     0.6769
INFO:causalml:   sMAPE   (Control):     0.5471
INFO:causalml:   sMAPE (Treatment):     0.1401
INFO:causalml:    Gini   (Control):     0.7299
INFO:causalml:    Gini (Treatment):     0.9951
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0612
INFO:causalml:    RMSE (Treatment):     0.7682
INFO:causalml:   sMAPE   (Control):     0.5225
INFO:causalml:   sMAPE (Treatment):     0.1526
INFO:causalml:    Gini   (Control):     0.7297
INFO:causalml:    Gini (Treatment):     0.9939

{'X':            W4        W2        W1        W3        W0        X1        X0  \
146 -0.706790 -0.165058 -1.348082  0.313898  0.513408 -0.992231 -0.989312
573  0.196341  0.991673 -0.825979  1.860407 -1.870355 -1.152153 -1.051394
314 -0.846455 -0.409130 -2.466592  0.522989 -0.052613 -2.655562  1.377086
370 -1.330248  0.617371 -1.086532  0.939956 -1.523047 -0.029272  0.894390
228 -0.479531  0.804877 -0.334062  0.447143  0.576800 -0.277841 -0.766331
..        ...       ...       ...       ...       ...       ...       ...
169 -0.409555 -0.737805 -3.028388  0.817463 -0.393057 -0.845180  1.640679
111  1.024268  0.556637 -0.514663  1.858435 -0.414872 -0.567619  0.793652
29  -1.026446  0.591365  0.862837  0.817784 -0.689067 -0.759426 -0.807769
948  0.137207  0.807432  0.012233 -0.321381 -1.727172 -0.265376  1.037937
525 -1.141040  0.446646 -0.843669  0.808363  0.327886 -0.739442  0.486219

           X0        X1
146 -0.989312 -0.992231
573 -1.051394 -1.152153
314  1.377086 -2.655562
370  0.894390 -0.029272
228 -0.766331 -0.277841
..        ...       ...
169  1.640679 -0.845180
111  0.793652 -0.567619
29  -0.807769 -0.759426
948  1.037937 -0.265376
525  0.486219 -0.739442

[800 rows x 9 columns], 'y': 146     3.922772
573     3.364299
314    -3.811119
370     6.533270
228     8.255998
         ...
169    -3.755154
111    16.735009
29      3.429318
948     9.814477
525     9.406466
Name: y, Length: 800, dtype: float64, 'treatment': 146     True
573     True
314    False
370     True
228     True
       ...
169    False
111     True
29      True
948     True
525     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
834 -0.921188  0.601325 -1.805151  0.607381  0.656701  0.552350  0.119174
421 -2.057703  0.279791 -0.650186 -0.436779 -0.108370  0.907991 -1.865056
63  -2.000224  0.352174  0.394109 -0.450750  0.766047 -0.466814  2.799461
407 -1.290075  0.523591 -0.037169 -0.751136  0.489787  1.070899  0.626482
216 -1.185130  1.723057 -2.436984 -0.947938 -0.797647 -1.237892  0.020300
..        ...       ...       ...       ...       ...       ...       ...
560 -0.591932  0.047587 -1.357618 -1.008455 -1.341175  0.311592  1.261070
7    0.174130  1.506210 -1.549595  0.782615 -1.878625 -0.626575  1.176085
370 -1.330248  0.617371 -1.086532  0.939956 -1.523047 -0.029272  0.894390
994  0.160794  0.460056  0.691403  0.008455 -0.133095 -1.589448  1.180637
962 -0.058529  0.632542 -0.738886 -0.663994 -0.868324 -2.555294 -0.708468

           X0        X1
834  0.119174  0.552350
421 -1.865056  0.907991
63   2.799461 -0.466814
407  0.626482  1.070899
216  0.020300 -1.237892
..        ...       ...
560  1.261070  0.311592
7    1.176085 -0.626575
370  0.894390 -0.029272
994  1.180637 -1.589448
962 -0.708468 -2.555294

[800 rows x 9 columns], 'y': 834    10.523346
421    -2.608507
63     15.814566
407    10.716455
216     2.376140
         ...
560    -7.215301
7      -2.646268
370     6.533270
994    13.409683
962     1.353868
Name: y, Length: 800, dtype: float64, 'treatment': 834     True
421     True
63      True
407     True
216     True
       ...
560    False
7      False
370     True
994     True
962     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.1340
INFO:causalml:    RMSE (Treatment):     0.7060
INFO:causalml:   sMAPE   (Control):     0.5439
INFO:causalml:   sMAPE (Treatment):     0.1519
INFO:causalml:    Gini   (Control):     0.7274
INFO:causalml:    Gini (Treatment):     0.9950
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True

{'X':            W4        W2        W1        W3        W0        X1        X0  \
629 -1.087554  1.775480  1.171119  0.254408  0.110085 -0.901117  0.490717
168  0.435794  1.361508 -0.020057 -1.105804 -0.948502 -0.816422  1.539983
637 -1.283359  0.832725  0.002037 -1.432698 -2.375726 -1.559718  1.057731
565  0.489147  1.219821 -1.070019  0.305099  0.624808 -0.830622  1.045480
610 -0.711808  1.595194  0.094204  0.005941 -1.302585 -0.669101  1.030998
..        ...       ...       ...       ...       ...       ...       ...
897 -0.501089 -1.031997 -1.334448 -0.012652 -0.451207 -0.315850 -0.369898
62  -0.387319  0.363055 -3.887119 -0.957211 -0.720283 -1.195020 -0.053329
527 -1.914933 -0.160798 -2.443913  0.459466  0.752528 -0.032713  0.745998
941 -0.858300  2.689664  0.060630 -0.800292 -0.649474 -0.542851  0.680252
391 -0.307159  1.106719  1.338069 -1.242240  0.718523  0.202760  0.500101

           X0        X1
629  0.490717 -0.901117
168  1.539983 -0.816422
637  1.057731 -1.559718
565  1.045480 -0.830622
610  1.030998 -0.669101
..        ...       ...
897 -0.369898 -0.315850
62  -0.053329 -1.195020
527  0.745998 -0.032713
941  0.680252 -0.542851
391  0.500101  0.202760

[800 rows x 9 columns], 'y': 629    10.711793
168    13.539036
637   -10.850731
565    17.014565
610     9.248446
         ...
897    -4.566144
62     -5.979631
527     8.043131
941     9.630585
391    13.502571
Name: y, Length: 800, dtype: float64, 'treatment': 629     True
168     True
637    False
565     True
610     True
       ...
897    False
62     False
527     True
941     True
391     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
384 -0.952465  0.527441 -1.232497 -0.135736 -0.788245  0.164010  0.849034
209 -0.133713  0.654669 -1.106384 -0.108285  1.071118 -1.245543 -0.793213
339 -0.174635  1.102320 -0.538718  1.353400 -2.411045 -0.088519 -0.223676
952 -0.623857  0.509187 -1.613917 -0.527539 -0.021147 -1.638291  1.771485
545 -1.156909  1.955408  0.619625  1.010345  0.520459  0.423056  0.779032
..        ...       ...       ...       ...       ...       ...       ...
839 -0.294596  0.983148 -0.666509  0.796590  0.072181 -0.815520  0.188710
10   0.846619  2.980613  0.205696  2.969570  0.858587 -1.222764  1.147817
734 -2.534543  1.297053 -0.227526 -0.605377 -1.703765 -0.280342  0.415044
941 -0.858300  2.689664  0.060630 -0.800292 -0.649474 -0.542851  0.680252
350 -0.426754  1.054619  1.005346  0.254238 -0.790041 -1.571358  0.573671

           X0        X1
384  0.849034  0.164010
209 -0.793213 -1.245543
339 -0.223676 -0.088519
952  1.771485 -1.638291
545  0.779032  0.423056
..        ...       ...
839  0.188710 -0.815520
10   1.147817 -1.222764
734  0.415044 -0.280342
941  0.680252 -0.542851
350  0.573671 -1.571358

[800 rows x 9 columns], 'y': 384    -5.168611
209     8.004465
339     4.815990
952    -2.868485
545    15.065277
         ...
839    10.718157
10     24.500212
734    -0.213830
941     9.630585
350     8.901755
Name: y, Length: 800, dtype: float64, 'treatment': 384    False
209     True
339     True
952    False
545     True
       ...
839     True
10      True
734     True
941     True
350     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    RMSE   (Control):     3.0399
INFO:causalml:    RMSE (Treatment):     0.7060
INFO:causalml:   sMAPE   (Control):     0.5135
INFO:causalml:   sMAPE (Treatment):     0.1434
INFO:causalml:    Gini   (Control):     0.7466
INFO:causalml:    Gini (Treatment):     0.9947
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9668
INFO:causalml:    RMSE (Treatment):     0.6953
INFO:causalml:   sMAPE   (Control):     0.5294
INFO:causalml:   sMAPE (Treatment):     0.1350
INFO:causalml:    Gini   (Control):     0.7468
INFO:causalml:    Gini (Treatment):     0.9948
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0586
INFO:causalml:    RMSE (Treatment):     0.6825
INFO:causalml:   sMAPE   (Control):     0.5465
INFO:causalml:   sMAPE (Treatment):     0.1460
INFO:causalml:    Gini   (Control):     0.7248
INFO:causalml:    Gini (Treatment):     0.9953

{'X':            W4        W2        W1        W3        W0        X1        X0  \
907 -0.596246  0.342098  0.422666  1.205834 -0.189522 -0.157053  1.625369
110 -1.807733  2.432036 -0.421479  1.445365 -0.460025  0.868047  0.873665
282 -0.070319 -0.099650  0.457073  1.593983 -1.547777 -1.467975  0.444530
35  -0.572119 -0.757998 -0.095580 -0.836271  0.268984 -1.592530 -0.137505
562 -1.449613  1.071250 -1.609878  0.729917  1.114471  1.063556 -0.186103
..        ...       ...       ...       ...       ...       ...       ...
825 -1.429357 -0.888809  0.582119 -0.687709 -0.446310 -0.158307  0.811902
596 -1.135855  0.142313  0.627392  0.540314  0.120423 -1.359658  2.273243
293 -2.019915 -0.918739 -1.767926 -1.403425 -0.740373 -1.576824  0.279021
641 -2.239311  2.194117 -0.353902  0.360330  0.632691 -1.221853 -0.388297
516 -0.316795  2.424823  0.020510 -0.536910  0.983380 -0.880608  1.127091

           X0        X1
907  1.625369 -0.157053
110  0.873665  0.868047
282  0.444530 -1.467975
35  -0.137505 -1.592530
562 -0.186103  1.063556
..        ...       ...
825  0.811902 -0.158307
596  2.273243 -1.359658
293  0.279021 -1.576824
641 -0.388297 -1.221853
516  1.127091 -0.880608

[800 rows x 9 columns], 'y': 907    15.569400
110    11.934711
282     7.672536
35     -2.892738
562    10.482337
         ...
825     5.892887
596    14.773593
293   -11.609205
641     4.862547
516    16.717229
Name: y, Length: 800, dtype: float64, 'treatment': 907     True
110     True
282     True
35     False
562     True
       ...
825     True
596     True
293    False
641     True
516     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
503 -2.066140  2.033010 -1.559405  0.352297  0.418520 -0.333911  1.084480
527 -1.914933 -0.160798 -2.443913  0.459466  0.752528 -0.032713  0.745998
65  -2.860295  0.491337  0.307032  0.402665  0.265118 -1.899636 -0.565288
143 -2.040739  1.654124  2.203868  1.326991 -0.370663 -1.606991 -0.643100
808 -0.291951  1.043771 -0.571882 -0.537580  0.895934 -1.203433  1.200730
..        ...       ...       ...       ...       ...       ...       ...
351 -0.741034  0.497864 -0.483110  1.193559 -0.155819 -1.606313  0.005075
494 -1.309272  0.323194 -1.405781  0.614937  1.179140  0.560715 -0.047731
579 -0.385689  0.379145  0.280441  0.467014 -1.190009 -0.877053 -0.634747
220  0.757100  1.090061 -0.251709  0.571761 -1.084509 -1.752894  0.512210
336 -3.093855  1.469971  0.367279  1.174817 -0.729766 -2.114001  0.885400

           X0        X1
503  1.084480 -0.333911
527  0.745998 -0.032713
65  -0.565288 -1.899636
143 -0.643100 -1.606991
808  1.200730 -1.203433
..        ...       ...
351  0.005075 -1.606313
494 -0.047731  0.560715
579 -0.634747 -0.877053
220  0.512210 -1.752894
336  0.885400 -2.114001

[800 rows x 9 columns], 'y': 503    -3.163402
527     8.043131
65     -0.860515
143     3.430961
808    14.588358
         ...
351     7.214058
494    10.082742
579     3.518524
220    10.878908
336     2.926169
Name: y, Length: 800, dtype: float64, 'treatment': 503    False
527     True
65      True
143     True
808     True
       ...
351     True
494     True
579     True
220     True
336     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0257
INFO:causalml:    RMSE (Treatment):     0.6446
INFO:causalml:   sMAPE   (Control):     0.5649
INFO:causalml:   sMAPE (Treatment):     0.1381
INFO:causalml:    Gini   (Control):     0.7243
INFO:causalml:    Gini (Treatment):     0.9956
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
625 -1.336550  0.261883  0.557248 -0.069210  1.161863 -0.761143  1.075224
760 -1.539521  0.393619  0.922067 -2.071477 -0.370860 -1.118111  1.021422
78  -1.150653  0.133818  0.632773  1.970008 -2.371912 -0.758077  0.049079
49  -0.982952  2.225363 -1.526931 -0.694241  0.029243 -0.455032  0.234971
75  -0.681840 -0.860902 -0.003540  0.004867 -0.711607  1.445133  2.210988
..        ...       ...       ...       ...       ...       ...       ...
7    0.174130  1.506210 -1.549595  0.782615 -1.878625 -0.626575  1.176085
710 -0.753746  2.934628 -1.147550 -0.196935 -0.921997 -0.625230 -0.292340
943 -2.252890  0.804815 -1.189588  0.399429 -1.924218 -1.450234  0.482038
271 -1.683731 -0.490214  1.077274  1.927335 -0.447638  1.027982 -0.317068
891 -0.294883 -0.574886 -1.010901  1.587116  0.836384  0.457966  2.071495

           X0        X1
625  1.075224 -0.761143
760  1.021422 -1.118111
78   0.049079 -0.758077
49   0.234971 -0.455032
75   2.210988  1.445133
..        ...       ...
7    1.176085 -0.626575
710 -0.292340 -0.625230
943  0.482038 -1.450234
271 -0.317068  1.027982
891  2.071495  0.457966

[800 rows x 9 columns], 'y': 625    12.619755
760    -7.109575
78      2.567970
49      8.274390
75     -4.842126
         ...
7      -2.646268
710     5.904376
943   -10.951371
271     6.385709
891    20.166272
Name: y, Length: 800, dtype: float64, 'treatment': 625     True
760    False
78      True
49      True
75     False
       ...
7      False
710     True
943    False
271     True
891     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
906 -1.727409  0.768134  0.127817  2.012317 -0.001440  0.690172  0.662514
98  -0.724707 -0.167508 -2.200046  1.316365 -0.652669  1.877820  0.372580
983 -1.330248  1.247409 -0.571754 -0.359552  0.349860 -0.123228 -0.031874
497 -1.333797  1.734853 -0.414480  0.677529 -1.691099 -1.330015  0.657088
583 -0.820218 -0.945137 -1.568877 -0.082810  0.614237 -0.346996  0.919443
..        ...       ...       ...       ...       ...       ...       ...
894 -0.029186  0.537780 -2.631535  0.408287 -0.885739 -0.202335  1.614458
780 -0.483565  0.853663  1.381569 -0.538695 -0.040407 -0.585683  0.004936
117 -2.185549  0.899307 -1.971894 -1.174155 -1.218568 -0.519142  1.033019
848 -1.892578  1.453947 -2.287147  0.205164 -0.295408 -1.312249  0.506585
258 -1.776837  0.805692 -1.001388  0.520858  0.798608 -1.470144  0.534863

           X0        X1
906  0.662514  0.690172
98   0.372580  1.877820
983 -0.031874 -0.123228
497  0.657088 -1.330015
583  0.919443 -0.346996
..        ...       ...
894  1.614458 -0.202335
780  0.004936 -0.585683
117  1.033019 -0.519142
848  0.506585 -1.312249
258  0.534863 -1.470144

[800 rows x 9 columns], 'y': 906    11.554534
98     -3.658929
983     7.361146
497    -5.843257
583    -2.819370
         ...
894    -2.776312
780     8.820757
117   -11.180244
848    -5.747920
258     8.016723
Name: y, Length: 800, dtype: float64, 'treatment': 906     True
98     False
983     True
497    False
583    False
       ...
894    False
780     True
117    False
848    False
258     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0735
INFO:causalml:    RMSE (Treatment):     0.7186
INFO:causalml:   sMAPE   (Control):     0.5432
INFO:causalml:   sMAPE (Treatment):     0.1394
INFO:causalml:    Gini   (Control):     0.7331
INFO:causalml:    Gini (Treatment):     0.9948
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0731
INFO:causalml:    RMSE (Treatment):     0.7347
INFO:causalml:   sMAPE   (Control):     0.5509
INFO:causalml:   sMAPE (Treatment):     0.1434
INFO:causalml:    Gini   (Control):     0.7232
INFO:causalml:    Gini (Treatment):     0.9945
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0618
INFO:causalml:    RMSE (Treatment):     0.7741
INFO:causalml:   sMAPE   (Control):     0.5257
INFO:causalml:   sMAPE (Treatment):     0.1482
INFO:causalml:    Gini   (Control):     0.7235
INFO:causalml:    Gini (Treatment):     0.9937
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator

{'X':            W4        W2        W1        W3        W0        X1        X0  \
379 -2.642522  2.168664 -0.282198 -0.922232 -0.027263 -1.608540  0.585088
91  -1.781143  1.133039 -0.366381 -1.016497  0.753451 -0.648109  0.902865
237 -0.843070  3.028032 -0.964293  0.319940 -2.199262 -0.535469 -1.279896
521 -1.235550  2.140683 -2.152255  0.310299 -1.837627 -0.705293 -0.465131
958 -0.495343  0.847053 -0.028871 -0.718100 -0.871586 -0.630299 -0.132512
..        ...       ...       ...       ...       ...       ...       ...
834 -0.921188  0.601325 -1.805151  0.607381  0.656701  0.552350  0.119174
323 -1.225390  1.130518 -1.096882 -0.350487 -0.604044  0.001088  0.390490
127 -1.184442  1.633850 -0.489806  0.806896 -0.654125 -0.324203 -1.297638
591 -1.762898  1.105254  1.394248  0.278984 -0.431230 -0.144092 -0.668399
768 -0.693216  0.942545 -0.176337 -1.216868 -1.459316  0.118780  0.014068

           X0        X1
379  0.585088 -1.608540
91   0.902865 -0.648109
237 -1.279896 -0.535469
521 -0.465131 -0.705293
958 -0.132512 -0.630299
..        ...       ...
834  0.119174  0.552350
323  0.390490  0.001088
127 -1.297638 -0.324203
591 -0.668399 -0.144092
768  0.014068  0.118780

[800 rows x 9 columns], 'y': 379    -6.731177
91      9.048832
237    -0.336643
521     0.541061
958     5.051010
         ...
834    10.523346
323     6.435152
127     2.218793
591     3.407731
768     3.776833
Name: y, Length: 800, dtype: float64, 'treatment': 379    False
91      True
237     True
521     True
958     True
       ...
834     True
323     True
127     True
591     True
768     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
66  -0.074892 -0.353977  0.148845 -0.275548 -0.150470  0.507088 -0.258207
411 -0.546985  0.632225  0.177670  1.152931 -1.341671 -1.043610  0.915882
714  0.484495  0.681690 -1.070190  1.380717  0.003125 -0.471048 -0.511469
697 -0.735466  0.746269 -1.122905  0.464823 -0.708806 -2.491421 -0.071736
359 -1.250892  0.608337 -1.159236  1.196291 -2.448155 -1.418204  2.171817
..        ...       ...       ...       ...       ...       ...       ...
883 -1.053935 -0.020818 -0.218070 -0.163484 -1.436398  0.888213  0.560956
677 -0.926078  0.166307 -2.183101  1.988610 -0.286812 -1.952046  0.131363
336 -3.093855  1.469971  0.367279  1.174817 -0.729766 -2.114001  0.885400
74  -0.996694  0.160173 -2.637286 -2.023282  1.303478 -1.990375  1.207325
14  -2.078772  1.196831  1.691383 -0.112157 -0.733879 -1.146159  0.723966

           X0        X1
66  -0.258207  0.507088
411  0.915882 -1.043610
714 -0.511469 -0.471048
697 -0.071736 -2.491421
359  2.171817 -1.418204
..        ...       ...
883  0.560956  0.888213
677  0.131363 -1.952046
336  0.885400 -2.114001
74   1.207325 -1.990375
14   0.723966 -1.146159

[800 rows x 9 columns], 'y': 66      8.421138
411     9.164325
714    10.878215
697     3.459264
359     7.556557
         ...
883     5.901248
677     6.044637
336     2.926169
74      8.688916
14      5.340331
Name: y, Length: 800, dtype: float64, 'treatment': 66     True
411    True
714    True
697    True
359    True
       ...
883    True
677    True
336    True
74     True
14     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0681
INFO:causalml:    RMSE (Treatment):     0.7303
INFO:causalml:   sMAPE   (Control):     0.5512
INFO:causalml:   sMAPE (Treatment):     0.1448
INFO:causalml:    Gini   (Control):     0.7432
INFO:causalml:    Gini (Treatment):     0.9948
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9306
INFO:causalml:    RMSE (Treatment):     0.6556
INFO:causalml:   sMAPE   (Control):     0.5388
INFO:causalml:   sMAPE (Treatment):     0.1402
INFO:causalml:    Gini   (Control):     0.7361
INFO:causalml:    Gini (Treatment):     0.9956

{'X':            W4        W2        W1        W3        W0        X1        X0  \
900 -1.528877  0.854363 -0.020892 -0.125090  1.283825  0.408115 -0.168281
446  0.595650  0.365987 -0.116439  0.995399  0.154264  1.021106 -2.210810
699 -1.175823  0.350680  0.033996  0.794509 -0.475092 -2.358432  0.259472
70  -0.652798  1.861460 -2.041953 -1.200137  0.846213 -0.817692  1.894073
933 -0.934060  1.319778  0.959669 -0.733035 -1.291453 -0.773287  0.716901
..        ...       ...       ...       ...       ...       ...       ...
578 -0.125015 -0.484719 -0.554158 -0.644919 -0.352951 -0.169403  0.807828
365 -0.079718 -0.263842  0.747338  0.929554  0.426811 -1.590757  1.366447
765 -0.608825  1.705784  0.327906 -0.288734 -0.692755  1.172959  0.656402
607 -0.713550 -0.806284 -0.339457 -0.279553 -0.477920 -0.690686 -0.221325
95  -0.058838  4.762058  0.823520  0.244461 -0.340888 -2.698332 -0.572900

           X0        X1
900 -0.168281  0.408115
446 -2.210810  1.021106
699  0.259472 -2.358432
70   1.894073 -0.817692
933  0.716901 -0.773287
..        ...       ...
578  0.807828 -0.169403
365  1.366447 -1.590757
765  0.656402  1.172959
607 -0.221325 -0.690686
95  -0.572900 -2.698332

[800 rows x 9 columns], 'y': 900     9.560536
446     6.907692
699     4.759513
70     15.668385
933     6.639250
         ...
578    -2.946371
365    15.195618
765    11.910646
607     3.571733
95      9.600613
Name: y, Length: 800, dtype: float64, 'treatment': 900     True
446     True
699     True
70      True
933     True
       ...
578    False
365     True
765     True
607     True
95      True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
65  -2.860295  0.491337  0.307032  0.402665  0.265118 -1.899636 -0.565288
670 -0.325648 -0.215383 -0.051446  0.066123  0.154725 -0.186235 -0.145738
829 -1.396963 -0.080609 -2.047189 -0.910429 -1.164537 -1.251936 -0.347075
684 -2.035070  2.267671  0.292658  0.472886  1.087179 -0.826693  0.637336
959 -1.282991  1.134722 -1.881201  0.830477  0.965414  1.414359 -1.140110
..        ...       ...       ...       ...       ...       ...       ...
355 -0.326494  2.433717  0.080853  1.915810 -0.164674  0.466589 -0.152236
35  -0.572119 -0.757998 -0.095580 -0.836271  0.268984 -1.592530 -0.137505
314 -0.846455 -0.409130 -2.466592  0.522989 -0.052613 -2.655562  1.377086
383 -1.820459  0.011031 -1.003686 -0.301110  0.254573 -1.724935  0.863482
104 -1.505922  2.182535 -1.410477  1.379506 -1.351655 -1.104962 -1.837318

           X0        X1
65  -0.565288 -1.899636
670 -0.145738 -0.186235
829 -0.347075 -1.251936
684  0.637336 -0.826693
959 -1.140110  1.414359
..        ...       ...
355 -0.152236  0.466589
35  -0.137505 -1.592530
314  1.377086 -2.655562
383  0.863482 -1.724935
104 -1.837318 -1.104962

[800 rows x 9 columns], 'y': 65     -0.860515
670     8.500958
829    -9.531238
684    11.451222
959     7.562645
         ...
355    13.712619
35     -2.892738
314    -3.811119
383     5.450868
104    -2.668188
Name: y, Length: 800, dtype: float64, 'treatment': 65      True
670     True
829    False
684     True
959     True
       ...
355     True
35     False
314    False
383     True
104     True
Name: v0, Length: 800, dtype: bool}

INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0845
INFO:causalml:    RMSE (Treatment):     0.7984
INFO:causalml:   sMAPE   (Control):     0.5368
INFO:causalml:   sMAPE (Treatment):     0.1555
INFO:causalml:    Gini   (Control):     0.7325
INFO:causalml:    Gini (Treatment):     0.9935
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True

{'X':            W4        W2        W1        W3        W0        X1        X0  \
498  0.922735  1.290530  0.397031  0.589139 -0.007310  0.062392  0.668905
461 -1.364660 -1.842826  0.241333  0.373454 -0.367738  0.860921  0.880397
213 -0.961749  1.214165 -0.354784  1.375875 -1.675979 -0.633450  0.130199
280 -0.924446 -0.495998 -1.067446 -0.475160 -0.560881 -1.593906  1.057462
872 -0.675589  1.480966  0.633872 -1.812491  0.279114 -0.021202  0.937137
..        ...       ...       ...       ...       ...       ...       ...
583 -0.820218 -0.945137 -1.568877 -0.082810  0.614237 -0.346996  0.919443
692 -1.425142 -0.090768 -2.394217  0.281658 -1.069567 -1.236345 -1.514344
824 -2.113843  0.954802 -0.116011  0.031653  0.087367 -0.731206  1.268051
319 -0.246796  1.042340  0.473929 -1.882334 -1.252649 -0.129804 -2.165228
968 -0.626450  0.610291 -0.847638  0.077436  0.480144 -1.839511  0.703036

           X0        X1
498  0.668905  0.062392
461  0.880397  0.860921
213  0.130199 -0.633450
280  1.057462 -1.593906
872  0.937137 -0.021202
..        ...       ...
583  0.919443 -0.346996
692 -1.514344 -1.236345
824  1.268051 -0.731206
319 -2.165228 -0.129804
968  0.703036 -1.839511

[800 rows x 9 columns], 'y': 498    17.581110
461    -6.434741
213     5.306177
280     5.994137
872    11.901044
         ...
583    -2.819370
692    -8.079772
824     8.772335
319    -2.951778
968    10.038489
Name: y, Length: 800, dtype: float64, 'treatment': 498     True
461    False
213     True
280     True
872     True
       ...
583    False
692    False
824     True
319     True
968     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
566 -0.600353  1.068704 -1.045374  0.760096 -0.036793 -1.155525  0.884924
726 -0.283535  0.621638 -0.999520  0.843142  1.790317 -0.030857  0.902776
272 -3.198219  0.554460  0.179309  2.032221 -0.401181 -1.528464  1.415503
356 -1.087973  1.063452 -0.582690 -0.391019 -0.717769  0.567377  0.968256
836 -0.185908  0.768699 -0.919715 -1.103215 -1.382410 -0.970128 -0.209856
..        ...       ...       ...       ...       ...       ...       ...
644  0.074273 -0.584974 -0.371922  0.791947 -2.236616  0.159478  1.327895
803  0.354704  1.494915  0.648519  0.097670 -0.697552  0.274267  0.820103
186 -0.858757  3.823995  0.218085  1.742811 -1.001666 -0.271821  1.026534
359 -1.250892  0.608337 -1.159236  1.196291 -2.448155 -1.418204  2.171817
333 -0.903943  1.303046 -0.373566  0.414038  0.837871 -0.085945  0.009761

           X0        X1
566  0.884924 -1.155525
726  0.902776 -0.030857
272  1.415503 -1.528464
356  0.968256  0.567377
836 -0.209856 -0.970128
..        ...       ...
644  1.327895  0.159478
803  0.820103  0.274267
186  1.026534 -0.271821
359  2.171817 -1.418204
333  0.009761 -0.085945

[800 rows x 9 columns], 'y': 566    11.504404
726    18.301691
272    -7.247960
356    -4.713297
836     2.849093
         ...
644     9.476615
803    14.674849
186    14.738530
359     7.556557
333    11.223406
Name: y, Length: 800, dtype: float64, 'treatment': 566     True
726     True
272    False
356    False
836     True
       ...
644     True
803     True
186     True
359     True
333     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    RMSE   (Control):     2.9304
INFO:causalml:    RMSE (Treatment):     0.6669
INFO:causalml:   sMAPE   (Control):     0.5375
INFO:causalml:   sMAPE (Treatment):     0.1398
INFO:causalml:    Gini   (Control):     0.7641
INFO:causalml:    Gini (Treatment):     0.9957
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9838
INFO:causalml:    RMSE (Treatment):     0.6644
INFO:causalml:   sMAPE   (Control):     0.5334
INFO:causalml:   sMAPE (Treatment):     0.1405
INFO:causalml:    Gini   (Control):     0.7088
INFO:causalml:    Gini (Treatment):     0.9953
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0108
INFO:causalml:    RMSE (Treatment):     0.6659
INFO:causalml:   sMAPE   (Control):     0.5188
INFO:causalml:   sMAPE (Treatment):     0.1441
INFO:causalml:    Gini   (Control):     0.7486

{'X':            W4        W2        W1        W3        W0        X1        X0  \
580 -0.197310  2.008508 -0.347956  0.087102  0.032457 -2.205103 -0.149105
22   0.539595  1.318356  0.301499  3.369823  0.047571  1.204919  1.920096
87  -1.405182  1.380866 -2.273438  1.029827  0.528251 -0.012064 -0.114884
522  0.488747  0.260763 -0.084712  2.012467  0.136177 -0.514404  1.454348
844 -0.961302  1.353622  0.111644  1.015867 -2.161583  0.255054  0.858101
..        ...       ...       ...       ...       ...       ...       ...
364 -1.078590  1.563574  1.504080 -0.486241 -1.540033 -0.435152  0.956055
643 -0.837543  2.690282 -0.956587  1.519004 -3.148478  1.188059  1.128666
692 -1.425142 -0.090768 -2.394217  0.281658 -1.069567 -1.236345 -1.514344
582 -1.856578  1.519025 -0.354817  1.777607 -0.103456 -0.540906  0.894554
535 -0.498853 -0.438894 -0.539694  0.645447 -0.372969 -1.473858  2.897329

           X0        X1
580 -0.149105 -2.205103
22   1.920096  1.204919
87  -0.114884 -0.012064
522  1.454348 -0.514404
844  0.858101  0.255054
..        ...       ...
364  0.956055 -0.435152
643  1.128666  1.188059
692 -1.514344 -1.236345
582  0.894554 -0.540906
535  2.897329 -1.473858

[800 rows x 9 columns], 'y': 580     8.433110
22     25.918390
87      8.347375
522    19.196033
844    -5.710027
         ...
364     7.693260
643     9.118838
692    -8.079772
582    10.549091
535    16.319324
Name: y, Length: 800, dtype: float64, 'treatment': 580     True
22      True
87      True
522     True
844    False
       ...
364     True
643     True
692    False
582     True
535     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
532 -1.066751  1.332414 -0.908307 -0.420243 -0.283136 -1.052984  0.072030
995 -2.515948  0.660984 -1.169142 -2.272146 -1.210642 -0.284190 -0.127452
816 -0.262987  0.921280 -0.389069 -0.883456 -2.158824 -1.767588 -0.312548
335 -1.086530  1.994553  0.585116 -0.719867 -2.473053  1.434212  0.722229
533 -1.983985  2.080045 -0.821934 -0.160942 -2.473228 -1.513175 -1.154217
..        ...       ...       ...       ...       ...       ...       ...
3   -2.330312  1.552707 -1.004874  1.042000  0.342380 -1.363540  1.515319
303 -1.789335  1.176506  0.016001  1.199059  0.834791 -0.485443  0.705215
998 -0.107884  0.866378 -1.644002 -0.046340 -1.318515  0.888535  1.662692
119 -0.412019 -1.707939  0.133539  0.898397 -0.839980 -0.812084  1.197569
613 -1.417773 -0.153505  0.258187  0.222832 -1.176441 -0.629369  1.426766

           X0        X1
532  0.072030 -1.052984
995 -0.127452 -0.284190
816 -0.312548 -1.767588
335  0.722229  1.434212
533 -1.154217 -1.513175
..        ...       ...
3    1.515319 -1.363540
303  0.705215 -0.485443
998  1.662692  0.888535
119  1.197569 -0.812084
613  1.426766 -0.629369

[800 rows x 9 columns], 'y': 532     5.559274
995   -13.283829
816    -0.020121
335     6.324474
533    -6.739186
         ...
3      10.343818
303    11.687300
998    13.321122
119     9.223485
613     7.422234
Name: y, Length: 800, dtype: float64, 'treatment': 532     True
995    False
816     True
335     True
533     True
       ...
3       True
303     True
998     True
119     True
613     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    Gini (Treatment):     0.9954
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0193
INFO:causalml:    RMSE (Treatment):     0.7014
INFO:causalml:   sMAPE   (Control):     0.5388
INFO:causalml:   sMAPE (Treatment):     0.1490
INFO:causalml:    Gini   (Control):     0.7211
INFO:causalml:    Gini (Treatment):     0.9949
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0

{'X':            W4        W2        W1        W3        W0        X1        X0  \
199 -0.887451 -0.781149 -0.243760 -0.813839 -0.018489 -1.809168  1.396317
249 -0.735623  0.814306 -1.693160  1.847391 -0.791449 -0.970486 -1.068904
75  -0.681840 -0.860902 -0.003540  0.004867 -0.711607  1.445133  2.210988
629 -1.087554  1.775480  1.171119  0.254408  0.110085 -0.901117  0.490717
719 -1.646363  0.269526 -1.677945  1.596705 -1.646524  0.649971 -0.577936
..        ...       ...       ...       ...       ...       ...       ...
503 -2.066140  2.033010 -1.559405  0.352297  0.418520 -0.333911  1.084480
123 -0.755355  1.402171 -0.488604  1.421476 -2.454651  1.055428 -1.229143
425 -0.547299  1.010457  0.355019  0.509403 -0.299352 -1.751637  1.623652
153 -0.787940  1.279546 -1.034601  0.763826 -0.810432 -1.157911 -0.536019
180 -0.772968  0.508917 -1.168432 -0.936875  1.844232 -0.008911  0.298550

           X0        X1
199  1.396317 -1.809168
249 -1.068904 -0.970486
75   2.210988  1.445133
629  0.490717 -0.901117
719 -0.577936  0.649971
..        ...       ...
503  1.084480 -0.333911
123 -1.229143  1.055428
425  1.623652 -1.751637
153 -0.536019 -1.157911
180  0.298550 -0.008911

[800 rows x 9 columns], 'y': 199     8.221818
249    -2.008335
75     -4.842126
629    10.711793
719     0.828056
         ...
503    -3.163402
123     1.168663
425    13.323890
153     3.964156
180    12.418586
Name: y, Length: 800, dtype: float64, 'treatment': 199     True
249    False
75     False
629     True
719     True
       ...
503    False
123     True
425     True
153     True
180     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     3.0336
INFO:causalml:    RMSE (Treatment):     0.7137
INFO:causalml:   sMAPE   (Control):     0.5254
INFO:causalml:   sMAPE (Treatment):     0.1430
INFO:causalml:    Gini   (Control):     0.7298
INFO:causalml:    Gini (Treatment):     0.9946
INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator
INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0
INFO:causalml:Error metrics for group True
INFO:causalml:    RMSE   (Control):     2.9814
INFO:causalml:    RMSE (Treatment):     0.6610
INFO:causalml:   sMAPE   (Control):     0.5544
INFO:causalml:   sMAPE (Treatment):     0.1489
INFO:causalml:    Gini   (Control):     0.6981

{'X':            W4        W2        W1        W3        W0        X1        X0  \
772 -1.631390  0.465915 -1.149010  1.590925 -0.376748 -1.413846 -0.295266
75  -0.681840 -0.860902 -0.003540  0.004867 -0.711607  1.445133  2.210988
897 -0.501089 -1.031997 -1.334448 -0.012652 -0.451207 -0.315850 -0.369898
197 -1.572938  0.609204 -2.470786  2.406344 -0.928691 -0.722715  1.007663
650 -1.089175  0.031213 -2.627317  1.213359  0.277555 -1.571450  0.129622
..        ...       ...       ...       ...       ...       ...       ...
112  0.060647  0.642094  1.051395  1.234630 -0.601046  0.340267  2.262831
818 -2.294772  1.081263 -0.110948  0.240523 -2.452061 -0.676310  1.024324
646 -0.626251  1.021268  0.186793  1.691995 -1.534219 -0.559809  0.720521
720 -0.178107  0.545358 -1.188979 -2.009287 -1.712708  0.183073  2.293672
593 -0.197920  1.086551 -0.777233 -1.511006 -0.413144  0.015110  0.344828

           X0        X1
772 -0.295266 -1.413846
75   2.210988  1.445133
897 -0.369898 -0.315850
197  1.007663 -0.722715
650  0.129622 -1.571450
..        ...       ...
112  2.262831  0.340267
818  1.024324 -0.676310
646  0.720521 -0.559809
720  2.293672  0.183073
593  0.344828  0.015110

[800 rows x 9 columns], 'y': 772     3.250030
75     -4.842126
897    -4.566144
197    -4.810420
650    -2.393578
         ...
112    20.053296
818   -11.770683
646     9.399353
720    11.018665
593     8.505994
Name: y, Length: 800, dtype: float64, 'treatment': 772     True
75     False
897    False
197    False
650    False
       ...
112     True
818    False
646     True
720     True
593     True
Name: v0, Length: 800, dtype: bool}
{'X':            W4        W2        W1        W3        W0        X1        X0  \
14  -2.078772  1.196831  1.691383 -0.112157 -0.733879 -1.146159  0.723966
309 -0.539594  2.000693  1.298256  0.596046 -2.741050 -0.539698  0.178355
185 -0.265206  0.795345 -1.677192 -0.822158  0.585555 -0.521756 -0.615447
212 -0.919538  1.656161  0.432204  0.358171 -0.174663 -1.124815  1.438214
467 -1.523227  1.571773  0.543944  0.010920  1.160325 -0.031949  0.427831
..        ...       ...       ...       ...       ...       ...       ...
475 -1.371406 -1.789706 -1.298206 -1.611236 -0.709442 -0.195347  2.724470
248 -0.322096  0.956739 -1.722687  0.445099  1.192956 -0.951015 -0.322611
867 -1.435952  0.855195  0.715461 -0.153104 -1.408541 -0.723836  0.896810
458 -1.265210 -0.125523 -0.769733  1.467806 -1.007910  0.213974  0.547473
711  0.099864  0.069601 -0.148899  0.008564 -0.642494 -0.050277  0.035692

           X0        X1
14   0.723966 -1.146159
309  0.178355 -0.539698
185 -0.615447 -0.521756
212  1.438214 -1.124815
467  0.427831 -0.031949
..        ...       ...
475  2.724470 -0.195347
248 -0.322611 -0.951015
867  0.896810 -0.723836
458  0.547473  0.213974
711  0.035692 -0.050277

[800 rows x 9 columns], 'y': 14      5.340331
309     4.869483
185    -0.302411
212    13.226200
467    12.156180
         ...
475   -10.586297
248    10.511960
867     5.611633
458     7.125670
711     8.687897
Name: y, Length: 800, dtype: float64, 'treatment': 14      True
309     True
185    False
212     True
467     True
       ...
475    False
248     True
867     True
458     True
711     True
Name: v0, Length: 800, dtype: bool}

INFO:causalml:    Gini (Treatment):     0.9955
INFO:dowhy.causal_refuters.data_subset_refuter:Making use of Bootstrap as we have more than 100 examples.
                 Note: The greater the number of examples, the more accurate are the confidence estimates

Refutation Values

refuter_count = 1
if inspect_refutations is True:
    for refutation in refutation_list:
        print("####### Refutation {}#######################################################################################".format(refuter_count))
        print("*** Class Name ***")
        print()
        print(refutation.refutation_type)
        print()
        print(refutation)
        print("########################################################################################################")
        print()
        refuter_count += 1

####### Refutation 1#######################################################################################
*** Class Name ***

Refute: Bootstrap Sample Dataset

Refute: Bootstrap Sample Dataset
Estimated effect:12.89367255934708
New effect:12.374411274559812
p value:0.28

########################################################################################################

####### Refutation 2#######################################################################################
*** Class Name ***

Refute: Use a subset of data

Refute: Use a subset of data
Estimated effect:12.89367255934708
New effect:12.616442370428834
p value:0.31000000000000005

########################################################################################################

####### Refutation 3#######################################################################################
*** Class Name ***

Refute: Bootstrap Sample Dataset

Refute: Bootstrap Sample Dataset
Estimated effect:12.075122341243496
New effect:12.111367745920434
p value:0.44

########################################################################################################

####### Refutation 4#######################################################################################
*** Class Name ***

Refute: Use a subset of data

Refute: Use a subset of data
Estimated effect:12.075122341243496
New effect:12.063766705476722
p value:0.47

########################################################################################################

####### Refutation 5#######################################################################################
*** Class Name ***

Refute: Bootstrap Sample Dataset

Refute: Bootstrap Sample Dataset
Estimated effect:[11.29290641]
New effect:11.336360019179892
p value:[0.44]

########################################################################################################

####### Refutation 6#######################################################################################
*** Class Name ***

Refute: Use a subset of data

Refute: Use a subset of data
Estimated effect:[11.29290641]
New effect:11.283369137714823
p value:[0.47]

########################################################################################################