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...

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Veröffentlicht in:Sociological methods & research Jg. 53; H. 3; S. 1071 - 1104
Hauptverfasser: Cinelli, Carlos, Forney, Andrew, Pearl, Judea
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Angeles, CA SAGE Publications 01.08.2024
SAGE PUBLICATIONS, INC
Schlagworte:
Causal models
Discrepancies
Econometrics
Frustration
Regression (Statistics)
Statistics
DAG
causal inference
bad controls
regression
back-door criterion
ISSN:0049-1241, 1552-8294
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0049-1241
1552-8294
DOI:10.1177/00491241221099552