dowhy.causal_refuters package

Submodules

dowhy.causal_refuters.add_unobserved_common_cause module

class dowhy.causal_refuters.add_unobserved_common_cause.AddUnobservedCommonCause(*args, **kwargs)[source]

Bases: CausalRefuter

Add an unobserved confounder for refutation.

Supports additional parameters that can be specified in the refute_estimate() method.

‘confounders_effect_on_treatment’: how the simulated confounder affects the value of treatment. This can be linear (for continuous treatment) or binary_flip (for binary treatment)
‘confounders_effect_on_outcome’: how the simulated confounder affects the value of outcome. This can be linear (for continuous outcome) or binary_flip (for binary outcome)
‘effect_strength_on_treatment’: parameter for the strength of the effect of simulated confounder on treatment. For linear effect, it is the regression coeffient. For binary_flip, it is the probability that simulated confounder’s effect flips the value of treatment from 0 to 1 (or vice-versa).
‘effect_strength_on_outcome’: parameter for the strength of the effect of simulated confounder on outcome. For linear effect, it is the regression coeffient. For binary_flip, it is the probability that simulated confounder’s effect flips the value of outcome from 0 to 1 (or vice-versa).

TODO: Needs an interpretation module

Initialize the parameters required for the refuter.

If effect_strength_on_treatment or effect_strength_on_outcome is not given, it is calculated automatically as a range between the minimum and maximum effect strength of observed confounders on treatment and outcome respectively.

Parameters

confounders_effect_on_treatment – str : The type of effect on the treatment due to the unobserved confounder. Possible values are [‘binary_flip’, ‘linear’]
confounders_effect_on_outcome – str : The type of effect on the outcome due to the unobserved confounder. Possible values are [‘binary_flip’, ‘linear’]
effect_strength_on_treatment – float, numpy.ndarray: This refers to the strength of the confounder on treatment. For a linear effect, it behaves like the regression coeffecient. For a binary flip it is the probability with which it can invert the value of the treatment.
effect_strength_on_outcome – float, numpy.ndarray: This refers to the strength of the confounder on outcome. For a linear effect, it behaves like the regression coefficient. For a binary flip, it is the probability with which it can invert the value of the outcome.
effect_fraction_on_treatment – float: If effect_strength_on_treatment is not provided, this parameter decides the effect strength of the simulated confounder as a fraction of the effect strength of observed confounders on treatment. Defaults to 1.
effect_fraction_on_outcome – float: If effect_strength_on_outcome is not provided, this parameter decides the effect strength of the simulated confounder as a fraction of the effect strength of observed confounders on outcome. Defaults to 1.
plotmethod – string: Type of plot to be shown. If None, no plot is generated. This parameter is used only only when more than one treatment confounder effect values or outcome confounder effect values are provided. Default is “colormesh”. Supported values are “contour”, “colormesh” when more than one value is provided for both confounder effect value parameters; “line” when provided for only one of them.
simulated_method_name – method type to add unobserved common cause. “linear-partial-R2” for linear sensitivity analysis
percent_change_estimate – It is the percentage of reduction of treatment estimate that could alter the results (default = 1) if percent_change_estimate = 1, the robustness value describes the strength of association of confounders with treatment and outcome in order to reduce the estimate by 100% i.e bring it down to 0.
confounder_increases_estimate – True implies that confounder increases the absolute value of estimate and vice versa. (Default = False)
benchmark_common_causes – names of variables for bounding strength of confounders
significance_level – confidence interval for statistical inference(default = 0.05)
null_hypothesis_effect – assumed effect under the null hypothesis
plot_estimate – Generate contour plot for estimate while performing sensitivity analysis. (default = True). To override the setting, set plot_estimate = False.

generate_confounder_from_residuals(c1, c2, d_y, d_t, X)[source]

This function takes the residuals from the treatment and outcome model and their coefficients and simulates the intermediate random variable U by taking the row wise normal distribution corresponding to each residual value and then debiasing the intermediate variable to get the final variable.

Parameters: c1 – coefficient to the residual from the outcome model

:type float :param c2: coefficient to the residual from the treatment model :type float :param d_y: residuals from the outcome model :type list :param d_t: residuals from the treatment model :type list

Returns: The simulated values of the unobserved confounder based on the data

:type pandas.core.series.Series

include_confounders_effect(new_data, kappa_t, kappa_y)[source]

This function deals with the change in the value of the data due to the effect of the unobserved confounder. In the case of a binary flip, we flip only if the random number is greater than the threshold set. In the case of a linear effect, we use the variable as the linear regression constant.

Parameters

new_data – pandas.DataFrame: The data to be changed due to the effects of the unobserved confounder.
kappa_t – numpy.float64: The value of the threshold for binary_flip or the value of the regression coefficient for linear effect.
kappa_y – numpy.float64: The value of the threshold for binary_flip or the value of the regression coefficient for linear effect.

Returns

pandas.DataFrame: The DataFrame that includes the effects of the unobserved confounder.

include_simulated_confounder(convergence_threshold=0.1, c_star_max=1000)[source]

This function simulates an unobserved confounder based on the data using the following steps: 1. It calculates the “residuals” from the treatment and outcome model i.) The outcome model has outcome as the dependent variable and all the observed variables including treatment as independent variables ii.) The treatment model has treatment as the dependent variable and all the observed variables as independent variables.

2. U is an intermediate random variable drawn from the normal distribution with the weighted average of residuals as mean and a unit variance U ~ N(c1*d_y + c2*d_t, 1) where *d_y and d_t are residuals from the treatment and outcome model *c1 and c2 are coefficients to the residuals

The final U, which is the simulated unobserved confounder is obtained by debiasing the intermediate variable U by residualising it with X

Choosing the coefficients c1 and c2: The coefficients are chosen based on these basic assumptions: 1. There is a hyperbolic relationship satisfying c1*c2 = c_star 2. c_star is chosen from a range of possible values based on the correlation of the obtained simulated variable with outcome and treatment. 3. The product of correlations with treatment and outcome should be at a minimum distance to the maximum correlations with treatment and outcome in any of the observed confounders 4. The ratio of the weights should be such that they maintain the ratio of the maximum possible observed coefficients within some confidence interval

Parameters

c_star_max – The maximum possible value for the hyperbolic curve on which the coefficients to the residuals lie. It defaults to 1000 in the code if not specified by the user. :type int
convergence_threshold – The threshold to check the plateauing of the correlation while selecting a c_star. It defaults to 0.1 in the code if not specified by the user :type float

Returns

The simulated values of the unobserved confounder based on the data :type pandas.core.series.Series

infer_default_kappa_t(len_kappa_t=10)[source]: Infer default effect strength of simulated confounder on treatment.

infer_default_kappa_y(len_kappa_y=10)[source]: Infer default effect strength of simulated confounder on treatment.

refute_estimate()[source]

This function attempts to add an unobserved common cause to the outcome and the treatment. At present, we have implemented the behavior for one dimensional behaviors for continuous and binary variables. This function can either take single valued inputs or a range of inputs. The function then looks at the data type of the input and then decides on the course of action.

Returns: CausalRefuter: An object that contains the estimated effect and a new effect and the name of the refutation used.

dowhy.causal_refuters.bootstrap_refuter module

class dowhy.causal_refuters.bootstrap_refuter.BootstrapRefuter(*args, **kwargs)[source]

Bases: CausalRefuter

Refute an estimate by running it on a random sample of the data containing measurement error in the confounders. This allows us to find the ability of the estimator to find the effect of the treatment on the outcome.

It supports additional parameters that can be specified in the refute_estimate() method.

Parameters

num_simulations (int, optional) – The number of simulations to be run, CausalRefuter.DEFAULT_NUM_SIMULATIONS by default
sample_size (int, optional) – The size of each bootstrap sample and is the size of the original data by default
required_variables (int, list, bool, optional) – The list of variables to be used as the input for y~f(W) This is True by default, which in turn selects all variables leaving the treatment and the outcome

An integer argument refers to how many variables will be used for estimating the value of the outcome
A list explicitly refers to which variables will be used to estimate the outcome Furthermore, it gives the ability to explictly select or deselect the covariates present in the estimation of the outcome. This is done by either adding or explicitly removing variables from the list as shown below:

Note

We need to pass required_variables = [W0,W1] if we want W0 and W1.
We need to pass required_variables = [-W0,-W1] if we want all variables excluding W0 and W1.

If the value is True, we wish to include all variables to estimate the value of the outcome.

Warning

A False value is INVALID and will result in an error.

Parameters

noise (float, optional) – The standard deviation of the noise to be added to the data and is BootstrapRefuter.DEFAULT_STD_DEV by default
probability_of_change (float, optional) – It specifies the probability with which we change the data for a boolean or categorical variable It is noise by default, only if the value of noise is less than 1.
random_state (int, RandomState, optional) – The seed value to be added if we wish to repeat the same random behavior. For this purpose, we repeat the same seed in the psuedo-random generator.

DEFAULT_NUMBER_OF_TRIALS = 1

DEFAULT_STD_DEV = 0.1

DEFAULT_SUCCESS_PROBABILITY = 0.5

refute_estimate(*args, **kwargs)[source]

dowhy.causal_refuters.data_subset_refuter module

class dowhy.causal_refuters.data_subset_refuter.DataSubsetRefuter(*args, **kwargs)[source]

Bases: CausalRefuter

Refute an estimate by rerunning it on a random subset of the original data.

Supports additional parameters that can be specified in the refute_estimate() method.

Parameters

subset_fraction (float, optional) – Fraction of the data to be used for re-estimation, which is DataSubsetRefuter.DEFAULT_SUBSET_FRACTION by default.
num_simulations (int, optional) – The number of simulations to be run, which is CausalRefuter.DEFAULT_NUM_SIMULATIONS by default
random_state (int, RandomState, optional) – The seed value to be added if we wish to repeat the same random behavior. If we with to repeat the same behavior we push the same seed in the psuedo-random generator

DEFAULT_SUBSET_FRACTION = 0.8

refute_estimate()[source]

dowhy.causal_refuters.dummy_outcome_refuter module

class dowhy.causal_refuters.dummy_outcome_refuter.DummyOutcomeRefuter(*args, **kwargs)[source]

Bases: CausalRefuter

Refute an estimate by replacing the outcome with a simulated variable for which the true causal effect is known.

In the simplest case, the dummy outcome is an independent, randomly generated variable. By definition, the true causal effect should be zero.

More generally, the dummy outcome uses the observed relationship between confounders and outcome (conditional on treatment) to create a more realistic outcome for which the treatment effect is known to be zero. If the goal is to simulate a dummy outcome with a non-zero true causal effect, then we can add an arbitrary function h(t) to the dummy outcome’s generation process and then the causal effect becomes h(t=1)-h(t=0).

Note that this general procedure only works for the backdoor criterion.

1. We find f(W) for a each value of treatment. That is, keeping the treatment constant, we fit a predictor to estimate the effect of confounders W on outcome y. Note that since f(W) simply defines a new DGP for the simulated outcome, it need not be the correct structural equation from W to y. 2. We obtain the value of dummy outcome as: y_dummy = h(t) + f(W)

To prevent overfitting, we fit f(W) for one value of T and then use it to generate data for other values of t. Future support for identification based on instrumental variable and mediation.

If we originally started out with

       W
    /    \
    t --->y

On estimating the following with constant t,
y_dummy = f(W)

       W
    /     \
    t --|->y

This ensures that we try to capture as much of W--->Y as possible

On adding h(t)

       W
    /    \
    t --->y
      h(t)

Supports additional parameters that can be specified in the refute_estimate() method.

Parameters

num_simulations (int, optional) – The number of simulations to be run, which defaults to CausalRefuter.DEFAULT_NUM_SIMULATIONS
transformation_list (list, optional) –
It is a list of actions to be performed to obtain the outcome, which defaults to DummyOutcomeRefuter.DEFAULT_TRANSFORMATION. The default transformation is as follows:

[("zero",""),("noise", {'std_dev':1} )]

Each of the actions within a transformation is one of the following types:

function argument: function pd.Dataframe -> np.ndarray

It takes in a function that takes the input data frame as the input and outputs the outcome variable. This allows us to create an output varable that only depends on the covariates and does not depend on the treatment variable.

string argument

Currently it supports some common estimators like

Linear Regression

K Nearest Neighbours

Support Vector Machine

Neural Network

Random Forest

Or functions such as:

Permute This permutes the rows of the outcome, disassociating any effect of the treatment on the outcome.

Noise This adds white noise to the outcome with white noise, reducing any causal relationship with the treatment.

Zero It replaces all the values in the outcome by zero

Examples:
The transformation_list is of the following form:

If the function pd.Dataframe -> np.ndarray is already defined. [(func,func_params),('permute',{'permute_fraction':val}),('noise',{'std_dev':val})]

Every function should be able to support a minimum of two arguments X_train and outcome_train which correspond to the training data and the outcome that we want to predict, along with additional parameters such as the learning rate or the momentum constant can be set with the help of func_args.

[(neural_network,{'alpha': 0.0001, 'beta': 0.9}),('permute',{'permute_fraction': 0.2}),('noise',{'std_dev': 0.1})]

The neural network is invoked as neural_network(X_train, outcome_train, **args).

If a function from the above list is used [('knn',{'n_neighbors':5}), ('permute', {'permute_fraction': val} ), ('noise', {'std_dev': val} )]

Parameters: true_causal_effect – A function that is used to get the True Causal Effect for the modelled dummy outcome. It defaults to DummyOutcomeRefuter.DEFAULT_TRUE_CAUSAL_EFFECT, which means that there is no relationship between the treatment and outcome in the dummy data.

Note

The true causal effect should take an input of the same shape as the treatment and the output should match the shape of the outcome

Parameters: required_variables – The list of variables to be used as the input for y~f(W) This is True by default, which in turn selects all variables leaving the treatment and the outcome

Note

We need to pass required_variables = [W0,W1] if we want W0 and W1.

We need to pass required_variables = [-W0,-W1] if we want all variables excluding W0 and W1.

If the value is True, we wish to include all variables to estimate the value of the outcome.

Warning

A False value is INVALID and will result in an error.

Note

These inputs are fed to the function for estimating the outcome variable. The same set of required_variables is used for each instance of an internal estimation function.

Parameters

bucket_size_scale_factor – For continuous data, the scale factor helps us scale the size of the bucket used on the data. The default scale factor is DummyOutcomeRefuter.DEFAULT_BUCKET_SCALE_FACTOR.
min_data_point_threshold (int, optional) – The minimum number of data points for an estimator to run. This defaults to DummyOutcomeRefuter.MIN_DATA_POINT_THRESHOLD. If the number of data points is too few for a certain category, we make use of the DummyOutcomeRefuter.DEFAULT_TRANSFORMATION for generaring the dummy outcome

DEFAULT_BUCKET_SCALE_FACTOR = 0.5

DEFAULT_NEW_DATA_WITH_UNOBSERVED_CONFOUNDING = None

DEFAULT_STD_DEV = 0.1

DEFAULT_TEST_FRACTION = [TestFraction(base=0.5, other=0.5)]

DEFAULT_TRANSFORMATION = [('zero', ''), ('noise', {'std_dev': 1})]

DEFAULT_TRUE_CAUSAL_EFFECT()

MIN_DATA_POINT_THRESHOLD = 30

SUPPORTED_ESTIMATORS = ['linear_regression', 'knn', 'svm', 'random_forest', 'neural_network']

noise(outcome, std_dev)[source]

Add white noise with mean 0 and standard deviation = std_dev

Parameters

'outcome' – np.ndarray The outcome variable, to which the white noise is added.
'std_dev' – float The standard deviation of the white noise.

Returns

outcome with added noise

permute(outcome, permute_fraction)[source]

If the permute_fraction is 1, we permute all the values in the outcome. Otherwise we make use of the Fisher Yates shuffle. Refer to https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle for more details.

Parameters

'outcome' – np.ndarray The outcome variable to be permuted.
'permute_fraction' – float [0, 1] The fraction of rows permuted.

preprocess_data_by_treatment()[source]

This function groups data based on the data type of the treatment.

Expected variable types supported for the treatment:

bool
pd.categorical
float
int

Returns: pandas.core.groupby.generic.DataFrameGroupBy

process_data(X_train, outcome_train, X_validation, outcome_validation, transformation_list)[source]

We process the data by first training the estimators in the transformation_list on X_train and outcome_train. We then apply the estimators on X_validation to get the value of the dummy outcome, which we store in outcome_validation.

Parameters

X_train (np.ndarray) – The data of the covariates which is used to train an estimator. It corresponds to the data of a single category of the treatment
outcome_train (np.ndarray) – This is used to hold the intermediate values of the outcome variable in the transformation list

For Example:

[ ('permute', {'permute_fraction': val} ), (func,func_params)]

The value obtained from permutation is used as an input for the custom estimator.

Parameters

X_validation (np.ndarray) – The data of the covariates that is fed to a trained estimator to generate a dummy outcome
outcome_validation (np.ndarray) – This variable stores the dummy_outcome generated by the transformations
transformation_list (np.ndarray) – The list of transformations on the outcome data required to produce a dummy outcome

refute_estimate()[source]

class dowhy.causal_refuters.dummy_outcome_refuter.TestFraction(base, other)

Bases: tuple

Create new instance of TestFraction(base, other)

base: Alias for field number 0

other: Alias for field number 1

dowhy.causal_refuters.graph_refuter module

class dowhy.causal_refuters.graph_refuter.GraphRefutation(method_name_discrete, method_name_continuous)[source]

Bases: CausalRefutation

Class for storing the result of a refutation method.

add_conditional_independence_test_result(number_of_constraints_model, number_of_constraints_satisfied, refutation_result)[source]

class dowhy.causal_refuters.graph_refuter.GraphRefuter(data, method_name_discrete='conditional_mutual_information', method_name_continuous='partial_correlation')[source]

Bases: CausalRefuter

Class for performing refutations on graph and storing the results

Initialize data for graph refutation

:param data:input dataset :param method_name_discrete: name of method for testing conditional independence in discrete data :param method_name_continuous: name of method for testing conditional independece in continuous data :returns : instance of GraphRefutation class

conditional_mutual_information(x=None, y=None, z=None)[source]

partial_correlation(x=None, y=None, z=None)[source]

refute_model(independence_constraints)[source]

Method to test conditional independence using the graph refutation object on the given testing set

Parameters: independence_constraints – List of implications to test the conditional independence on

:returns : GraphRefutation object

set_refutation_result(number_of_constraints_model)[source]: Method to set the result for graph refutation. Set true if there are no false implications else false

dowhy.causal_refuters.linear_sensitivity_analyzer module

class dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer(estimator=None, data=None, treatment_name=None, percent_change_estimate=1.0, significance_level=0.05, confounder_increases_estimate=True, benchmark_common_causes=None, null_hypothesis_effect=0, frac_strength_treatment=None, frac_strength_outcome=None, common_causes_order=None)[source]

Bases: object

Class to perform sensitivity analysis See: https://carloscinelli.com/files/Cinelli%20and%20Hazlett%20(2020)%20-%20Making%20Sense%20of%20Sensitivity.pdf

Parameters

estimator – linear estimator of the causal model
data – Pandas dataframe
treatment_name –
name of treatment :param percent_change_estimate: It is the percentage of reduction of treatment estimate that could alter the results (default = 1)

if percent_change_estimate = 1, the robustness value describes the strength of association of confounders with treatment and outcome in order to reduce the estimate by 100% i.e bring it down to 0.
null_hypothesis_effect – assumed effect under the null hypothesis
confounder_increases_estimate – True implies that confounder increases the absolute value of estimate and vice versa. (Default = True)
benchmark_common_causes – names of variables for bounding strength of confounders
significance_level – confidence interval for statistical inference(default = 0.05)
frac_strength_treatment – strength of association between unobserved confounder and treatment compared to benchmark covariate
frac_strength_outcome – strength of association between unobserved confounder and outcome compared to benchmark covariate
common_causes_order – The order of column names in OLS regression data

check_sensitivity(plot=True)[source]

Function to perform sensitivity analysis. :param plot: plot = True generates a plot of point estimate and the variations with respect to unobserved confounding.

plot = False overrides the setting

Returns: instance of LinearSensitivityAnalyzer class

compute_bias_adjusted(r2tu_w, r2yu_tw)[source]

Computes the bias adjusted estimate, standard error, t-value, partial R2, confidence intervals

Parameters

r2tu_w – partial r^2 from regressing unobserved confounder u on treatment t after conditioning on observed covariates w
r2yu_tw – partial r^2 from regressing unobserved confounder u on outcome y after conditioning on observed covariates w and treatment t

Returns

Python dictionary with information about partial R^2 of confounders with treatment and outcome and bias adjusted variables

partial_r2_func(estimator_model=None, treatment=None)[source]

Computes the partial R^2 of regression model

Parameters

estimator_model – Linear regression model
treatment – treatment name

Returns

partial R^2 value

plot(plot_type='estimate', critical_value=None, x_limit=0.8, y_limit=0.8, num_points_per_contour=200, plot_size=(7, 7), contours_color='blue', critical_contour_color='red', label_fontsize=9, contour_linewidths=0.75, contour_linestyles='solid', contours_label_color='black', critical_label_color='red', unadjusted_estimate_marker='D', unadjusted_estimate_color='black', adjusted_estimate_marker='^', adjusted_estimate_color='red', legend_position=(1.6, 0.6))[source]

Plots and summarizes the sensitivity bounds as a contour plot, as they vary with the partial R^2 of the unobserved confounder(s) with the treatment and the outcome Two types of plots can be generated, based on adjusted estimates or adjusted t-values X-axis: Partial R^2 of treatment and unobserved confounder(s) Y-axis: Partial R^2 of outcome and unobserved confounder(s) We also plot bounds on the partial R^2 of the unobserved confounders obtained from observed covariates

Parameters

plot_type – “estimate” or “t-value”
critical_value – special reference value of the estimate or t-value that will be highlighted in the plot
x_limit – plot’s maximum x_axis value (default = 0.8)
y_limit – plot’s minimum y_axis value (default = 0.8)
num_points_per_contour – number of points to calculate and plot each contour line (default = 200)
plot_size – tuple denoting the size of the plot (default = (7,7))
contours_color – color of contour line (default = blue) String or array. If array, lines will be plotted with the specific color in ascending order.
critical_contour_color – color of threshold line (default = red)
label_fontsize – fontsize for labelling contours (default = 9)
contour_linewidths – linewidths for contours (default = 0.75)
contour_linestyles – linestyles for contours (default = “solid”) See : https://matplotlib.org/3.5.0/gallery/lines_bars_and_markers/linestyles.html for more examples
contours_label_color – color of contour line label (default = black)
critical_label_color – color of threshold line label (default = red)
unadjusted_estimate_marker – marker type for unadjusted estimate in the plot (default = ‘D’) See: https://matplotlib.org/stable/api/markers_api.html
adjusted_estimate_marker – marker type for bias adjusted estimates in the plot (default = ‘^’)

Parm unadjusted_estimate_color

marker color for unadjusted estimate in the plot (default = “black”)

Parm adjusted_estimate_color

marker color for bias adjusted estimates in the plot (default = “red”)

:param legend_position:tuple denoting the position of the legend (default = (1.6, 0.6))

plot_estimate(r2tu_w, r2yu_tw)[source]

Computes the contours, threshold line and bounds for plotting estimates. Contour lines (z - axis) correspond to the adjusted estimate values for different values of r2tu_w (x) and r2yu_tw (y). :param r2tu_w: hypothetical partial R^2 of confounder with treatment(x - axis) :param r2yu_tw: hypothetical partial R^2 of confounder with outcome(y - axis)

Returns

contour_values : values of contour lines for the plot critical_estimate : threshold point estimate_bounds : estimate values for unobserved confounders (bias adjusted estimates)

plot_t(r2tu_w, r2yu_tw)[source]

Computes the contours, threshold line and bounds for plotting t. Contour lines (z - axis) correspond to the adjusted t values for different values of r2tu_w (x) and r2yu_tw (y). :param r2tu_w: hypothetical partial R^2 of confounder with treatment(x - axis) :param r2yu_tw: hypothetical partial R^2 of confounder with outcome(y - axis)

Returns

contour_values : values of contour lines for the plot critical_t : threshold point t_bounds : t-value for unobserved confounders (bias adjusted t values)

robustness_value_func(alpha=1.0)[source]

Function to calculate the robustness value. It is the minimum strength of association that confounders must have with treatment and outcome to change conclusions. Robustness value describes how strong the association must be in order to reduce the estimated effect by (100 * percent_change_estimate)%. Robustness value close to 1 means the treatment effect can handle strong confounders explaining almost all residual variation of the treatment and the outcome. Robustness value close to 0 means that even very weak confounders can also change the results.

Parameters: alpha – confidence interval (default = 1)
Returns: robustness value

treatment_regression()[source]

Function to perform regression with treatment as outcome

Returns: new OLS regression model

dowhy.causal_refuters.placebo_treatment_refuter module

class dowhy.causal_refuters.placebo_treatment_refuter.PlaceboTreatmentRefuter(*args, **kwargs)[source]

Bases: CausalRefuter

Refute an estimate by replacing treatment with a randomly-generated placebo variable.

Supports additional parameters that can be specified in the refute_estimate() method.

Parameters

placebo_type (str, optional) – Default is to generate random values for the treatment. If placebo_type is “permute”, then the original treatment values are permuted by row.
num_simulations (int, optional) – The number of simulations to be run, which is CausalRefuter.DEFAULT_NUM_SIMULATIONS by default
random_state (int, RandomState, optional) – The seed value to be added if we wish to repeat the same random behavior. If we want to repeat the same behavior we push the same seed in the psuedo-random generator.

DEFAULT_MEAN_OF_NORMAL = 0

DEFAULT_NUMBER_OF_TRIALS = 1

DEFAULT_PROBABILITY_OF_BINOMIAL = 0.5

DEFAULT_STD_DEV_OF_NORMAL = 0

refute_estimate()[source]

dowhy.causal_refuters.random_common_cause module

class dowhy.causal_refuters.random_common_cause.RandomCommonCause(*args, **kwargs)[source]

Bases: CausalRefuter

Refute an estimate by introducing a randomly generated confounder (that may have been unobserved).

Parameters

num_simulations (int, optional) – The number of simulations to be run, which is CausalRefuter.DEFAULT_NUM_SIMULATIONS by default
random_state (int, RandomState, optional) – The seed value to be added if we wish to repeat the same random behavior. If we with to repeat the same behavior we push the same seed in the psuedo-random generator

refute_estimate()[source]

Module contents

dowhy.causal_refuters.get_class_object(method_name, *args, **kwargs)[source]