Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes Page 1
Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes Federico …

Cluster-randomized experiments are increasingly used to evaluate interventions in routine
practice conditions, and researchers often adopt model-based methods with covariate …

This paper studies covariate adjusted estimation of the average treatment effect in stratified
experiments. We work in a general framework that includes matched tuples designs, coarse …

This paper studies the efficient estimation of a large class of treatment effect parameters that
arise in the analysis of experiments. Here, efficiency is understood to be with respect to a …

This article studies inference for the local average treatment effect in randomized controlled
trials with imperfect compliance where treatment status is determined according to “matched …

This paper studies inference in two-stage randomized experiments under covariate-adaptive
randomization. In the initial stage of this experimental design, clusters (eg, households …

Blocking, a special case of rerandomization, is routinely implemented in the design stage of
randomized experiments to balance the baseline covariates. This study proposes a …

How should a monopolistic seller (she) sell an item to a buyer (he), when the buyer's later
demands are affected by the earlier allocations he receives? For example, consider an …

Our paper discovers a new trade-off of using regression adjustments (RAs) in causal
inference under covariate-adaptive randomizations (CARs). On one hand, RAs can improve …

This dissertation studies statistical inference in randomized experiments, extending
important covariate-adaptive randomization tools to three commonly used experimental …