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

Music City Center 

Studying racial bias in policing is a critically important problem, but one that involves inherent difficulties due to the nature of 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 in neighborhoods primarily made up of individuals of other races. We develop an estimand to study this rigorously, outline assumptions necessary for causal identification, and conduct sensitivity analyses to assess robustness to key assumption violations. Additionally, we investigate difficulties with existing estimands targeting racial bias in policing. We show that estimation for these estimands, and those developed in this manuscript, benefits from incorporating mobility data into analyses. We apply these ideas to a study in New York City, finding substantial racial bias, race and place policing, and robustness 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.

Causal inference

Mobility data

Racial discrimination

Race and place

Sensitivity analysis 

Social Statistics Section