A Crash Course in Good and Bad Controls
A Crash Course in Good and Bad Controls
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DOI：
10.1177/00491241221099552
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发表时间：
2022-05-20
影响因子：
6.3
通讯作者：
Pearl, Judea
中科院分区：
文献类型：
--
作者：
Cinelli, Carlos;Forney, Andrew;Pearl, Judea
许多统计学和计量经济学的学生对传统文献中被称为“不良控制”的问题的处理方式表示失望。当向回归方程中添加一个变量时，回归系数与该系数打算表示的效果之间产生了意想不到的差异，就会出现问题。避免这种差异对所有数据密集型科学的分析师来说都是一个挑战。本文通过一系列说明性示例描述了用于理解、可视化和解决问题的图形化工具。通过将这个“速成班”提供给教师和实践者，我们希望将这些工具应用到更广泛的关注回归模型因果解释的科学家群体中。
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.