A Crash Course in Good and Bad Controls

被引：336

作者：

Cinelli, Carlos ^{[1
]}

Forney, Andrew ^{[2
]}

Pearl, Judea ^{[3
]}

机构：

[1] Univ Washington, Dept Stat, Seattle, WA 98195 USA

[2] Loyola Marymount Univ, Dept Comp Sci, Los Angeles, CA 90045 USA

[3] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA

来源：

SOCIOLOGICAL METHODS & RESEARCH | 2024年 / 53卷 / 03期

基金：

美国国家科学基金会;

关键词：

causal inference; bad controls; back-door criterion; DAG; regression; CAUSAL INFERENCE; DIAGRAMS; MODELS; BIAS; IDENTIFICATION; SELECTION;

D O I：

10.1177/00491241221099552

中图分类号：

O1 [数学]; C [社会科学总论];

学科分类号：

03 ; 0303 ; 0701 ; 070101 ;

摘要：

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.

引用

页码：1071 / 1104

页数：34

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