We find that black drivers are stopped more often than white drivers relative to their share of the driving-age population, but that Hispanic drivers are stopped less often than whites.
Expanding on MR, we propose Model Class Reliance (MCR) as the upper and lower bounds on the degree to which any well-performing prediction model within a class may rely on a variable of interest, or set of variables of interest.
We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges.
Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous under- standing of why it performs so well on difficult problems and how it is best applied in practice.
We look at common problems found in data that is used for predictive modeling tasks, and describe how to address them with the vtreat R package.
We extend balloon and sample-smoothing estimators, two types of variable-bandwidth kernel density estimators, by a shift parameter and derive their asymptotic properties.
METHODOLOGY STATISTICS THEORY STATISTICS THEORY
We discuss how our reproducible WAR framework, built entirely on publicly available data, can be easily extended to estimate WAR for players at any position, provided that researchers have access to data specifying which players are on the field during each play.