Open ruiiliuu opened 2 years ago
I am using the ERUPT metric on a holdout dataset to compare different models, seems to make sense to me, namely out-of-sample comparison of treatment assignment policies conditioned on the CATE estimated by the different models.
Hi @amit-sharma. Thanks for the answer to @ruiiliuu as those were one of my questions as well. I'd suggest making it clear in the example notebook. In my project, I have Y (continuous) and T (continuous) with a bunch of confounders (X,W). In my case, I want to answer a question. What would happen to T if we move from T to T*0.95 in the next year? What I want is to have an estimate of this effect for each subject (N=165). How would I set it?
Hi, I'm new in the field of Causality and I would like to use your package for my current project. This is a really great toolbox for causal inference. But I have several questions when checking case studies.
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":{}}) print(dml_estimate)
Thanks in advance for the explanation.