The fast growing statistical literatures on matching methods in several disciplines offer the promise of causal inference without resort to the difficult-to-justify functional form assumptions inherent in commonly used parametric methods. However, these literatures also suffer from many diverse...

Missing data are frequently encountered in the statistical analysis of randomized experiments. In this paper, I propose statistical methods that can be used to analyze randomized experiments with a nonignorable missing binary outcome where the missing-data mechanism may depend on the unobserved...

Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and...

The estimation of racial disparities in various fields is often hampered by the lack of individual-level racial information. In many cases, the law prohibits the collection of such information to prevent direct racial discrimination. As a result, analysts have frequently adopted Bayesian...