Comments (1)
amit-sharma
commented on June 24, 2024
2
Yes, the functionality to specify control and treatment values for a continuous treatment variable is implemented now.
For an example, check out cell 7 this notebook: https://www.pywhy.org/dowhy/v0.10.1/example_notebooks/dowhy-conditional-treatment-effects.html
tval1, tval2 = 10, 20
linear_estimate = model.estimate_effect(identified_estimand,
method_name="backdoor.linear_regression",
control_value=tval1,
treatment_value=tval2)
For a non-linear relationship, the same API is used, just the estimator is changed.
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, # condition used for CATE
confidence_intervals=False,
method_params={"init_params":{'model_y':GradientBoostingRegressor(),
'model_t': GradientBoostingRegressor(),
"model_final":LassoCV(fit_intercept=False),
'featurizer':PolynomialFeatures(degree=1, include_bias=False)},
"fit_params":{}})
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