Using Multitask Gaussian Processes to estimate the effect of a targeted effort to remove firearms

Abstract

Gun violence is a critical public safety concern in the United States. In 2006 California implemented a unique firearm monitoring program, the Armed and Prohibited Persons System (APPS), to address gun violence in the state by identifying those firearm owners who become prohibited from owning a firearm due to federal and state law and confiscating their firearms. Our goal is to assess the effect of APPS on California murder rates using annual, state-level crime data across the US for the years before and after the introduction of the program. To do so, we adapt a non-parametric Bayesian approach, multitask Gaussian Processes (MTGPs), to the panel data setting. MTGPs allow for flexible and parsimonious panel data models that nest many existing approaches and allow for direct control over both dependence across time and dependence across units, as well as natural uncertainty quantification. We extend this approach to incorporate non-Normal outcomes, auxiliary covariates, and multiple outcome series, which are all important in our application. We also show that this approach has attractive Frequentist properties, including a representation as a weighting estimator with separate weights over units and time periods. Applying this approach, we find that the increased monitoring and enforcement from the APPS program substantially decreased gun-related homicides in California. * We would like to thank Naoki Egami, Max Gopelrud, Jacob Montgomery, Liyang Sun, and seminar participants at Polmeth 2021 for helpful comments and discussion. We are especially grateful to Eli Sherman for his involvement in an earlier version of this project.