Understanding heterogeneous treatment effects

Firstly, it’s important to note how the dowhy wrapper of econml has cut down on a few steps with the dowhy.fit method. Usually, when you build a CausalModel such as this one directly with dowhy, it has a method called identify_effect that derives the probability expression for the effect to be estimated (the identified estimand). In this case, this is called the Average Treatment Effect (ATE). Then, another method called estimate_effect takes this expression and the models it’s supposed to tie together (regression and propensity). With them, it computes both the ATE, , and CATE, , for every outcome i and treatment t. However, since we used the wrapper to fit the causal model, it automatically takes care of both the identification and estimation steps.

You can access the identified ATE with the identified_estimand_ property and the estimate results with the estimate_ property for the causal model. The code can be seen...