We study estimation of causal effects in staggered-rollout designs—that is, settings where
there is staggered treatment adoption and the timing of treatment is as good as randomly …

The past decade has witnessed an explosion of interest in research and education in causal
inference, due to its wide applications in biomedical research, social sciences, artificial …

Fisher's randomization test (frt) delivers exact p-values under the strong null hypothesis of
no treatment effect on any units whatsoever and allows for flexible covariate adjustment to …

In randomized clinical trials, adjustments for baseline covariates at both design and analysis
stages are highly encouraged by regulatory agencies. A recent trend is to use a model …

We consider the problem of estimating and inferring treatment effects in randomized
experiments. In practice, stratified randomization, or more generally, covariate-adaptive …

We consider the problem of variance reduction in randomized controlled trials, through the
use of covariates correlated with the outcome but independent of the treatment. We propose …

In randomized experiments, adjusting for observed features when estimating treatment
effects has been proposed as a way to improve asymptotic efficiency. However, among …

Imputing missing potential outcomes using an estimated regression function is a natural
idea for estimating causal effects. In the literature, estimators that combine imputation and …

Randomized experiments balance all covariates on average and are considered the gold
standard for estimating treatment effects. Chance imbalances are nonetheless common in …

There has been a growing interest in covariate adjustment in the analysis of randomized
controlled trials in past years. For instance, the US Food and Drug Administration recently …