DoWhy example on ihdp (Infant Health and Development Program) dataset
[1]:
# importing required libraries
import os, sys
sys.path.append(os.path.abspath("../../"))
import dowhy
from dowhy.do_why import CausalModel
import pandas as pd
import numpy as np
Loading Data
[2]:
data= pd.read_csv("https://raw.githubusercontent.com/AMLab-Amsterdam/CEVAE/master/datasets/IHDP/csv/ihdp_npci_1.csv", header = None)
col = ["treatment", "y_factual", "y_cfactual", "mu0", "mu1" ,]
for i in range(1,26):
col.append("x"+str(i))
data.columns = col
data.head()
[2]:
| treatment | y_factual | y_cfactual | mu0 | mu1 | x1 | x2 | x3 | x4 | x5 | ... | x16 | x17 | x18 | x19 | x20 | x21 | x22 | x23 | x24 | x25 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 5.599916 | 4.318780 | 3.268256 | 6.854457 | -0.528603 | -0.343455 | 1.128554 | 0.161703 | -0.316603 | ... | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 6.875856 | 7.856495 | 6.636059 | 7.562718 | -1.736945 | -1.802002 | 0.383828 | 2.244320 | -0.629189 | ... | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 2.996273 | 6.633952 | 1.570536 | 6.121617 | -0.807451 | -0.202946 | -0.360898 | -0.879606 | 0.808706 | ... | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 1.366206 | 5.697239 | 1.244738 | 5.889125 | 0.390083 | 0.596582 | -1.850350 | -0.879606 | -0.004017 | ... | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 1.963538 | 6.202582 | 1.685048 | 6.191994 | -1.045229 | -0.602710 | 0.011465 | 0.161703 | 0.683672 | ... | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 30 columns
1.Model
[3]:
# Create a causal model from the data and given common causes.
xs = ""
for i in range(1,26):
xs += ("x"+str(i)+"+")
model=CausalModel(
data = data,
treatment='treatment',
outcome='y_factual',
common_causes=xs.split('+')
)
WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.
INFO:dowhy.do_why:Model to find the causal effect of treatment ['treatment'] on outcome ['y_factual']
2.Identify
[4]:
#Identify the causal effect
identified_estimand = model.identify_effect()
INFO:dowhy.causal_identifier:Common causes of treatment and outcome:['', 'x21', 'x22', 'x9', 'x8', 'x11', 'x16', 'x25', 'x4', 'x5', 'x20', 'x10', 'x17', 'x13', 'x7', 'x2', 'x23', 'x3', 'x24', 'x1', 'x15', 'x14', 'x6', 'x19', 'x18', 'x12']
WARNING:dowhy.causal_identifier:There are unobserved common causes. Causal effect cannot be identified.
WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] y
INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]
3. Estimate (using different methods)
3.1 Using Linear Regression
[5]:
# Estimate the causal effect and compare it with Average Treatment Effect
estimate = model.estimate_effect(identified_estimand,
method_name="backdoor.linear_regression", test_significance=True
)
print(estimate)
print("Causal Estimate is " + str(estimate.value))
data_1 = data[data["treatment"]==1]
data_0 = data[data["treatment"]==0]
print("ATE", np.mean(data_1["y_factual"])- np.mean(data_0["y_factual"]))
INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator
INFO:dowhy.causal_estimator:b: y_factual~treatment+x21+x22+x9+x8+x11+x16+x25+x4+x5+x20+x10+x17+x13+x7+x2+x23+x3+x24+x1+x15+x14+x6+x19+x18+x12
*** Causal Estimate ***
## Target estimand
Estimand type: ate
### Estimand : 1
Estimand name: iv
No such variable found!
### Estimand : 2
Estimand name: backdoor
Estimand expression:
d
──────────(Expectation(y_factual|x21,x22,x9,x8,x11,x16,x25,x4,x5,x20,x10,x17,x
dtreatment
13,x7,x2,x23,x3,x24,x1,x15,x14,x6,x19,x18,x12))
Estimand assumption 1, Unconfoundedness: If U→treatment and U→y_factual then P(y_factual|treatment,x21,x22,x9,x8,x11,x16,x25,x4,x5,x20,x10,x17,x13,x7,x2,x23,x3,x24,x1,x15,x14,x6,x19,x18,x12,U) = P(y_factual|treatment,x21,x22,x9,x8,x11,x16,x25,x4,x5,x20,x10,x17,x13,x7,x2,x23,x3,x24,x1,x15,x14,x6,x19,x18,x12)
## Realized estimand
b: y_factual~treatment+x21+x22+x9+x8+x11+x16+x25+x4+x5+x20+x10+x17+x13+x7+x2+x23+x3+x24+x1+x15+x14+x6+x19+x18+x12
## Estimate
Value: 3.928671750872715
## Statistical Significance
p-value: <0.001
Causal Estimate is 3.92867175087
ATE 4.02112101243
3.2 Using Propensity Score Matching
[6]:
estimate = model.estimate_effect(identified_estimand,
method_name="backdoor.propensity_score_matching"
)
print("Causal Estimate is " + str(estimate.value))
print("ATE", np.mean(data_1["y_factual"])- np.mean(data_0["y_factual"]))
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator
INFO:dowhy.causal_estimator:b: y_factual~treatment+x21+x22+x9+x8+x11+x16+x25+x4+x5+x20+x10+x17+x13+x7+x2+x23+x3+x24+x1+x15+x14+x6+x19+x18+x12
Causal Estimate is 3.8436503200364402
ATE 4.02112101243
3.3 Using Propensity Score Stratification
[13]:
estimate = model.estimate_effect(identified_estimand,
method_name="backdoor.propensity_score_stratification", method_params={'num_strata'50, 'clipping_threshold':5}
)
print("Causal Estimate is " + str(estimate.value))
print("ATE", np.mean(data_1["y_factual"])- np.mean(data_0["y_factual"]))
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator
INFO:dowhy.causal_estimator:b: y_factual~treatment+x21+x22+x9+x8+x11+x16+x25+x4+x5+x20+x10+x17+x13+x7+x2+x23+x3+x24+x1+x15+x14+x6+x19+x18+x12
Causal Estimate is 4.0560672956
ATE 4.02112101243
3.4 Using Propensity Score Weighting
[8]:
estimate = model.estimate_effect(identified_estimand,
method_name="backdoor.propensity_score_weighting"
)
print("Causal Estimate is " + str(estimate.value))
print("ATE", np.mean(data_1["y_factual"])- np.mean(data_0["y_factual"]))
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y_factual~treatment+x21+x22+x9+x8+x11+x16+x25+x4+x5+x20+x10+x17+x13+x7+x2+x23+x3+x24+x1+x15+x14+x6+x19+x18+x12
Causal Estimate is 4.04761815345
ATE 4.02112101243
4. Refute
[9]:
refute_results=model.refute_estimate(identified_estimand, estimate,
method_name="random_common_cause")
print(refute_results)
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y_factual~treatment+x21+x22+x9+x8+x11+x16+x25+x4+x5+x20+x10+x17+x13+x7+x2+x23+x3+x24+x1+x15+x14+x6+x19+x18+x12+w_random
Refute: Add a Random Common Cause
Estimated effect:(4.0476181534545397,)
New effect:(4.0480367100453618,)
[10]:
res_placebo=model.refute_estimate(identified_estimand, estimate,
method_name="placebo_treatment_refuter", placebo_type="permute")
print(res_placebo)
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y_factual~placebo+x21+x22+x9+x8+x11+x16+x25+x4+x5+x20+x10+x17+x13+x7+x2+x23+x3+x24+x1+x15+x14+x6+x19+x18+x12
Refute: Use a Placebo Treatment
Estimated effect:(4.0476181534545397,)
New effect:(0.057511331649253705,)
4.3 Data Subset Refuter
[11]:
res_subset=model.refute_estimate(identified_estimand, estimate,
method_name="data_subset_refuter", subset_fraction=0.9)
print(res_subset)
INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator
INFO:dowhy.causal_estimator:b: y_factual~treatment+x21+x22+x9+x8+x11+x16+x25+x4+x5+x20+x10+x17+x13+x7+x2+x23+x3+x24+x1+x15+x14+x6+x19+x18+x12
Refute: Use a subset of data
Estimated effect:(4.0476181534545397,)
New effect:(4.0274748385128563,)