Document Type
Article
Publication Date
2022
Abstract
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.
Original Publication Citation
Cinelli, C., Forney, A., & Pearl, J. (2022). A Crash Course in Good and Bad Controls. Sociological Methods & Research, 53(3), 1071-1104. https://doi.org/10.1177/00491241221099552 (Original work published 2024)
Digital Commons @ LMU & LLS Citation
Forney, Andrew; Cinelli, Carlos; and Pearl, Judea, "A Crash Course in Good and Bad Controls" (2022). Computer Science Faculty Works. 49.
https://digitalcommons.lmu.edu/cs_fac/49
