Prize winner's essay reveals how causal inference can make sense of biased policing data

The lack of progress in identifying and addressing the racial disparities in law enforcement stems in part from inconsistent record-keeping and misleading statistical analyses of incomplete data, argues Dean Knox, the winner of the inaugural 2021 NOMIS & Science Young Explorer Award. In his prize-winning essay, Knox illustrates the value of applying new tools and statistical techniques to imperfect data to reveal the extent and severity of racial bias in policing. Despite decades of high-profile incidents of excessive force against minorities in the United States and growing demands for police reform, many policymakers and academics struggle to understand the nature of the issue. According to Knox, there is a pressing need for new methods to make sense of policing data, which is often rife with inaccuracies, selective reporting, and misleading information. In addition, most data on police-civilian interactions are collected and shared by the police agencies themselves and many interactions go unreported to the public. The use of imperfect or biased data like these can lead to contradictory results and undermine our understanding of policing. Here, Knox shows how applying causal inference – an increasingly important subfield of statistics that focuses on the range of possible interpretations of the data, rather than one single causal outcome – can be used to make sense of problematic policing data. In other disciplines with similar data challenges, the causal-inference framework has proved invaluable to these ends. However, according to Knox, in policing research, careful causal analysis remains the exception, not the rule.