The connection between racially prejudiced policing and politics has a long history in the United States. In the current period, police organizations have displayed unprecedented support for Republican presidential candidates, and both have organized against social movements focused on addressing racial disparities in police contact. Yet despite strong connections between law enforcement and party politics, we know almost nothing about the relationship between partisan identity and the behavior of police officers. Using millions of traffic stop records from the Florida Highway Patrol and linked voter records, the present study shows that White Republican officers exhibit a larger racial disparity than White Democratic officers in their propensity to search motorists whom they have stopped. This result is robust to an array of alternative empirical tests and holds across varying sociodemographic contexts. I also find that both White Republican and White Democratic officers grew more biased between 2012 and 2020, a period characterized by the rise of the Black Lives Matter movement and the election of Donald Trump.

Most causal inference methods consider counterfactual variables under interventions that set the exposure to a fixed value. With continuous or multi-valued treatments or exposures, such counterfactuals may be of little practical interest because no feasible intervention can be implemented that would bring them about. Longitudinal modified treatment policies (LMTPs) are a recently developed nonparametric alternative that yield effects of immediate practical relevance with an interpretation in terms of meaningful interventions such as reducing or increasing the exposure by a given amount. LMTPs also have the advantage that they can be designed to satisfy the positivity assumption required for causal inference. We present a novel sequential regression formula that identifies the LMTP causal effect, study properties of the LMTP statistical estimand such as the efficient influence function and the efficiency bound, and propose four different estimators. Two of our estimators are efficient, and one is sequentially doubly robust in the sense that it is consistent if, for each time point, either an outcome regression or a treatment mechanism is consistently estimated. We perform numerical studies of the estimators, and present the results of our motivating study on hypoxemia and mortality in intubated Intensive Care Unit (ICU) patients. Software implementing our methods is provided in the form of the open source R package lmtp freely available on GitHub and CRAN.