Social and Behavioral Sciences Commons^™

Improving Estimation Efficiency Via Regression-Adjustment In Covariate-Adaptive Randomizations With Imperfect Compliance, Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang

Research Collection School Of Economics

We study how to improve efficiency via regression adjustments with additional covariates under covariate-adaptive randomizations (CARs) when subject compliance is imperfect. We first establish the semiparametric efficiency bound for the local average treatment effect (LATE) under CARs. Second, we develop a general regression-adjusted LATE estimator which allows for parametric, nonparametric, and regularized adjustments. Even when the adjustments are misspecified, our proposed estimator is still consistent and asymptotically normal, and their inference method still achieves the exact asymptotic size under the null. When the adjustments are correctly specified, our estimator achieves the semiparametric efficiency bound. Third, we derive the optimal linear …