A Causal Framework for Observational Studies of Discrimination

IF 1.5 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Johann D. Gaebler, William Cai, Guillaume W. Basse, Ravi Shroff, Sharad Goel, J. Hill
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引用次数: 10

Abstract

Abstract In studies of discrimination, researchers often seek to estimate a causal effect of race or gender on outcomes. For example, in the criminal justice context, one might ask whether arrested individuals would have been subsequently charged or convicted had they been a different race. It has long been known that such counterfactual questions face measurement challenges related to omitted-variable bias, and conceptual challenges related to the definition of causal estimands for largely immutable characteristics. Another concern, which has been the subject of recent debates, is post-treatment bias: many studies of discrimination condition on apparently intermediate outcomes, like being arrested, that themselves may be the product of discrimination, potentially corrupting statistical estimates. There is, however, reason to be optimistic. By carefully defining the estimand—and by considering the precise timing of events—we show that a primary causal quantity of interest in discrimination studies can be estimated under an ignorability condition that may hold approximately in some observational settings. We illustrate these ideas by analyzing both simulated data and the charging decisions of a prosecutor’s office in a large county in the United States.
歧视观察性研究的因果框架
摘要在歧视研究中,研究人员经常试图估计种族或性别对结果的因果影响。例如,在刑事司法背景下,人们可能会问,如果被逮捕的人是不同的种族,他们随后是否会被指控或定罪。众所周知,这种反事实问题面临着与遗漏变量偏差相关的测量挑战,以及与定义基本不可变特征的因果估计相关的概念挑战。最近争论的另一个问题是治疗后偏见:许多关于歧视的研究以明显的中间结果为条件,比如被逮捕,这些结果本身可能是歧视的产物,可能会破坏统计估计。然而,我们有理由保持乐观。通过仔细定义估计需求,并考虑事件的确切时间,我们表明,在一些观测环境中,可以在不可忽略的条件下估计歧视研究中感兴趣的主要因果量。我们通过分析模拟数据和美国一个大县检察官办公室的指控决定来说明这些想法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistics and Public Policy
Statistics and Public Policy SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
3.20
自引率
6.20%
发文量
13
审稿时长
32 weeks
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