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When we seek to uncover the causal effect of X on Y using observational data, we often include control variables in the hope that it will allow us to uncover our parameter of interest. However, there is a lack of guidance on how to pick and implement controls. The inclusion of what is known as ”bad controls” and the failure to include ”good controls” might result in bias. This article presents accessible guidelines for choosing and using control variables to identify causal effects.
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Jonathan Doucette (2026) studied this question.
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