Causal inference and racial bias in policing: New estimands and the importance of mobility data
警务种族偏见的因果推断：新目标参数与流动数据的重要性AI 解读 ↓
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警务种族偏见的因果推断：新目标参数与流动数据的重要性
- 研究问题
- 如何因果分析个人种族与所在社区种族构成共同关联的警务差别，并评估流动数据缺失对种族偏见估计的影响？
- 方法与思路
- 作者定义种族与地点警务差异的目标参数，讨论因果识别假设及敏感性分析，并将人口流动信息纳入现有和新参数的估计。
- 材料陈述的贡献
- 摘要称纽约市应用发现种族偏见及种族与地点警务差异，对较大假设违反仍具稳健性，且流动数据明显影响估计结果。
- 阅读关注点
- 应核对种族相关干预和目标参数如何定义、流动数据如何刻画暴露人群，以及敏感性参数的含义与所覆盖的假设违反类型。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列出识别假设、数据细节和效应数值，作者所称稳健性需结合敏感性范围判断。
原文摘要
Studying racial bias in policing is a critically important problem, but one that comes with a number of inherent difficulties due to the nature of the available data. In this manuscript we tackle multiple key issues in the causal analysis of racial bias in policing. First, we formalize race and place policing, the idea that individuals of one race are policed differently when they are in neighborhoods primarily made up of individuals of other races. We develop an estimand to study this question rigorously, show the assumptions necessary for causal identification, and develop sensitivity analyses to assess robustness to violations of key assumptions. Additionally, we investigate difficulties with existing estimands targeting racial bias in policing. We show for these estimands, and the estimands developed in this manuscript, that estimation can benefit from incorporating mobility data into analyses. We apply these ideas to a study in New York City, where we find a large amount of racial bias, as well as race and place policing, and that these findings are robust to large violations of untestable assumptions. We additionally show that mobility data can make substantial impacts on the resulting estimates, suggesting it should be used whenever possible in subsequent studies.