A0449
Title: Covariate adjustment in randomized block experiments and rerandomized experiments
Authors: Yuehan Yang - Central University of Finance and Economics (China) [presenting]
Abstract: Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. A series of regression adjustment methods are proposed to efficiently estimate the average treatment effect in randomized block experiments with low- and high-dimensional covariates. The asymptotic properties of the proposed estimators are derived, and the conditions under which this estimator is more efficient than the unadjusted one are outlined. A conservative variance estimator is provided to facilitate valid inferences. The framework allows one treated or control unit in some blocks and heterogeneous propensity scores across blocks, thus including paired experiments and finely stratified experiments as special cases. Rerandomized experiments and a combination of blocking and rerandomization are further accommodated. Moreover, the analysis allows both the number of blocks and block sizes to tend to infinity, as well as heterogeneous treatment effects across blocks, without assuming a true outcome data-generating model. Simulation studies and two real-data analyses demonstrate the advantages of the proposed method.
