3. Estimate causal effect based on the identified estimand

DoWhy supports methods based on both back-door criterion and instrumental variables. It also provides a non-parametric confidence intervals and a permutation test for testing the statistical significance of obtained estimate.

3.1. Supported estimation methods

• Methods based on estimating the treatment assignment

  • Propensity-based Stratification

  • Propensity Score Matching

  • Inverse Propensity Weighting

• Methods based on estimating the outcome model

  • Linear Regression

  • Generalized Linear Models

• Methods based on the instrumental variable equation

  • Binary Instrument/Wald Estimator

  • Two-stage least squares

  • Regression discontinuity

• Methods for front-door criterion and general mediation

  • Two-stage linear regression

Examples of using these methods are in the Estimation methods notebook.

3.2. Using EconML and CausalML estimation methods in DoWhy

It is easy to call external estimation methods using DoWhy. Currently we support integrations with the EconML and CausalML packages. Here’s an example of estimating conditional treatment effects using EconML’s double machine learning estimator.

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LassoCV
from sklearn.ensemble import GradientBoostingRegressor
dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML",
                control_value = 0,
                treatment_value = 1,
                target_units = lambda df: df["X0"]>1,
                confidence_intervals=False,
                method_params={
                    "init_params":{'model_y':GradientBoostingRegressor(),
                                   'model_t': GradientBoostingRegressor(),
                                   'model_final':LassoCV(),
                                   'featurizer':PolynomialFeatures(degree=1, include_bias=True)},
                    "fit_params":{}}
                                        )

More examples are in the Conditional Treatment Effects with DoWhy notebook.