Lasso-adjusted treatment effect estimation under covariate-adaptive randomization
研究了在协变量自适应随机化实验中，使用Lasso回归调整大量基线协变量以估计处理效应的方法，提出了最优估计量和非参数一致方差估计，适用于模型误设和多种随机化方法。
Summary We consider the problem of estimating and inferring treatment effects in randomized experiments. In practice, stratified randomization, or more generally, covariate-adaptive randomization, is routinely used in the design stage to balance treatment allocations with respect to a few variables that are most relevant to the outcomes. Then, regression is performed in the analysis stage to adjust the remaining imbalances to yield more efficient treatment effect estimators. Building upon and unifying recent results obtained for ordinary-least-squares adjusted estimators under covariate-adaptive randomization, this paper presents a general theory of regression adjustment that allows for model mis-specification and the presence of a large number of baseline covariates. We exemplify the theory on two lasso-adjusted treatment effect estimators, both of which are optimal in their respective classes. In addition, nonparametric consistent variance estimators are proposed to facilitate valid inferences, which work irrespective of the specific randomization methods used. The robustness and improved efficiency of the proposed estimators are demonstrated through numerical studies.