Lasso-adjusted treatment effect estimation under covariate-adaptive randomization
协变量自适应随机化下的 Lasso 调整处理效应估计
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.…