Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations
This paper examines regression-adjusted estimation and inference of unconditional quantile treatment eﬀects (QTEs) under covariate-adaptive randomizations (CARs). Datasets from ﬁeld experiments usually contain extra baseline covariates in addition to the strata indicators. We propose to incorporate these extra covariates via auxiliary regressions in the estimation and inference of unconditional QTEs. We establish the consistency, limit distribution, and validity of the multiplier bootstrap of the QTE estimator under CARs.