Causal inference and racial bias in policing: New estimands and the importance of mobility data

TL;DR

This paper formalizes causal estimands to analyze racial bias in policing, emphasizing the importance of mobility data for robust estimation, and applies these methods to NYC data revealing significant bias.

Contribution

It introduces new estimands for race and place policing, develops sensitivity analyses, and demonstrates the value of mobility data in causal inference of racial bias.

Findings

Large racial bias detected in NYC policing

Mobility data significantly improves estimation robustness

Findings remain robust under violations of assumptions

Abstract

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.