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.

翻译：研究警务中的种族偏见是一个至关重要的问题，但由于可用数据的性质，该研究面临诸多固有困难。本文针对警务种族偏见因果分析中的多个关键问题展开探讨。首先，我们形式化了"种族与场所警务"的概念——即当某一种族的个体进入主要由其他种族个体构成的社区时，会面临差异化的警务对待。为此我们构建了一个用于严谨研究该问题的估计量，阐明了因果识别所需的前提假设，并开发了敏感性分析方法以评估关键假设被违反时的估计稳健性。此外，我们研究了现有警务种族偏见估计量存在的缺陷。通过理论证明，无论是既有估计量还是本文提出的新估计量，其估计效果均可通过引入流动数据得到显著提升。我们将这些方法应用于纽约市的实证研究，发现了大量种族偏见及"种族与场所警务"现象的存在，且这些发现在不可检验假设被严重违反时仍保持稳健。研究进一步表明，流动数据会对估计结果产生实质性影响，建议后续研究在条件允许时应尽可能纳入此类数据。