因果推断与警务中的种族偏见：新估计量及移动数据的重要性
Causal inference and racial bias in policing: New estimands and the importance of mobility data
AI总结：
本文提出研究警务中“种族与地点”偏见的新因果估计量与敏感性分析，并结合移动数据分析纽约市数据，证实显著种族偏见及移动数据对估计的重要作用。
摘要：
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，这一问题伴随着许多固有困难。在本手稿中，我们处理警务种族偏见因果分析中的多个关键问题。首先，我们将“种族与地点警务”形式化，这一观点是指，当某一种族的个体处于主要由其他种族个体组成的社区时，他们会受到不同方式的警务对待。我们开发了一个估计量，以严谨研究该问题，展示因果识别所需的假设，并开发敏感性分析来评估对关键假设受到违背时的稳健性。此外，我们调查了现有针对警务种族偏见的估计量所存在的困难。我们表明，对于这些估计量以及本手稿中开发的估计量，将移动数据纳入分析可以改进估计。我们将这些想法应用于纽约市的一项研究，在该研究中我们发现了大量种族偏见以及种族与地点警务，并且这些发现对于不可检验假设的严重违背具有稳健性。我们还表明，移动数据会对所得估计产生实质性影响，这表明后续研究应在可能时尽量使用移动数据。
英文摘要：
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.
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