Joshua Shea

I develop a framework to detect and measure racial bias in police traffic searches. Officers are evaluated individually, permitting unrestricted officer heterogeneity and nonrandom assignment of drivers to officers. By using a threshold model with random thresholds, the direction and intensity of bias can vary with the probability that a driver carries contraband. Sharp bounds on the intensity of bias are derived using bilinear programs. I evaluate 50 officers from the Metropolitan Nashville Police Department and find 6 officers to be biased. Estimates suggest that the intensity of bias varies with the probability that a driver carries contraband.

Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, psychology, and other fields. When there is unobserved heterogeneity in causal effects, standard linear IV estimators only represent effects for complier subpopulations (Imbens and Angrist, 1994). Marginal treatment effect (MTE) methods (Heckman and Vytlacil, 1999, 2005) allow researchers to use additional assumptions to extrapolate beyond complier subpopulations. We discuss a flexible framework for MTE methods based on linear regression and the generalized method of moments. We show how to implement the framework using the ivmte package for R.

We provide large sample distribution theory for support vector regression (SVR) with l1-norm, along with error bars for the SVR regression coefficients. Although a classical Wald confidence interval obtains from our theory, its implementation inherently depends on the choice of a tuning parameter which scales the variance estimate and thus the width of the error bars. We address this shortcoming by further proposing an alternative large sample inference method based on the inversion of a novel test statistic which displays competitive power properties and does not depend on the choice of a tuning parameter.