论文标题
在研究种族歧视时进行治疗后选择的注释
A note on post-treatment selection in studying racial discrimination in policing
论文作者
论文摘要
我们讨论用于研究警务种族歧视的一些因果估计。一个核心挑战是，并非所有警察遇到的遭遇都记录在行政数据集中，并向研究人员使用。一种可能的解决方案是考虑种族有条件对被警察拘留的平民的平均因果影响。我们发现，这种估计与因果推论中更熟悉的估计有很大不同，需要谨慎解释。我们建议在这种情况下使用一种估计和新的估计和新的风险比率，该因果风险比具有更透明的解释，需要较弱的识别假设。我们通过重新分析NYPD停止危险数据集来证明这一点。我们的重新分析表明，在行政记录中忽略治疗后选择的天真估计器可能严重低估了这些和类似数据中少数民族与白人之间警察暴力的差异。
We discuss some causal estimands used to study racial discrimination in policing. A central challenge is that not all police-civilian encounters are recorded in administrative datasets and available to researchers. One possible solution is to consider the average causal effect of race conditional on the civilian already being detained by the police. We find that such an estimand can be quite different from the more familiar ones in causal inference and needs to be interpreted with caution. We propose using an estimand new for this context -- the causal risk ratio, which has more transparent interpretation and requires weaker identification assumptions. We demonstrate this through a reanalysis of the NYPD Stop-and-Frisk dataset. Our reanalysis shows that the naive estimator that ignores the post-treatment selection in administrative records may severely underestimate the disparity in police violence between minorities and whites in these and similar data.
