As I document using evidence from a journal data repository that I manage, the datasets used in empirical work are getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues....

This paper studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so...

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naı̈ve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate...