**Which Variables Do I Need? **

I started this substack off wanting to discuss inexact matching, but I just think it would be helpful that before we get into the nuts and bolts of inexact matching, a clear explanation of how to achieve unconfoundedness may be useful. It comes up a lot, and I think sometimes there isn’t enough clear exposition about it out there, so I figured why not just do this now, and next week I’ll wrap up my inexact matching substack.

Most of us learned causal inference for the very first time in a stats class where we learned about “running regressions” and “controlling for covariates”. If the class was somewhat advanced, that introduction to the ordinary least squares formulas might even be followed by learning the Frisch-Waugh-Lovell theorem where we learned OLS was “partialing out” those extra variables effects so that we could focus just on the partial relationship between the covariate of interest and the outcome. And then if we went even further, we might t…

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