How to deal with observational data? about causalml HOT 1 CLOSED

In observational causal inference, the most important step is that of forming a clear understanding of the possible confounding variables for the causal relationship that you are trying to measure. As things stand, this can only be done by qualitatively reasoning about the specific problem that you're trying to solve, ideally with other people who are also knowledgeable of the problem. Causal ML or any other current software packages can't help you with this.

Once you've defined your set of confounding variables, you can use any of the variety of estimation methods out there. The most common one is a simple linear multiple regression with the confounders as covariates. You can use statsmodels, DoWhy, etc. The methods implemented in Causal ML (like X-learner, R-learner) will also work, but they're most likely an overkill.

from causalml.