A Crash Course in Good and Bad Controls

Many students of statistics and econometrics express frustration with the way a problem known as bad control is treated in the traditional literature. The issue 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 intended to represent. Avoiding such discrepancies presents a challenge 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. 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.

Automated summary

This paper introduces graphical tools for addressing the problem of bad control and aims to make these tools accessible to a broader community of scientists concerned with the causal interpretation of regression models.