A Crash Course in Good and Bad Controls

Abstract

Many students, especially in econometrics, express frustration with the way a problem known as “bad control” is evaded, if not mishandled, in the traditional literature. The problem arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is expected to represent. Avoiding such discrepancies presents a challenge not only to practitioners of econometrics, but to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. We have found that the examples presented here can serve as a powerful instructional device to supplement formal discussions of the problem. By making this “crash course” accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models.

Keywords: Bad Control, Regression, Causal Inference, Omitted Variable Bias

JEL Classification: C01, C21