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
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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.
AbstractList 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.
Author Cinelli, Carlos
Forney, Andrew
Pearl, Judea
Author_xml – sequence: 1
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  surname: Cinelli
  fullname: Cinelli, Carlos
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  givenname: Andrew
  orcidid: 0000-0002-6366-1290
  surname: Forney
  fullname: Forney, Andrew
– sequence: 3
  givenname: Judea
  surname: Pearl
  fullname: Pearl, Judea
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Issue 3
Keywords DAG
causal inference
bad controls
regression
back-door criterion
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Snippet Many students of statistics and econometrics express frustration with the way a problem known as “bad control” is treated in the traditional literature. The...
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SubjectTerms Causal models
Discrepancies
Econometrics
Frustration
Regression (Statistics)
Statistics
Title A Crash Course in Good and Bad Controls
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