3 citations · 7 across the 4 of their papers we have counts for
5 papers
Adjustment with Many Regressors Under Covariate-Adaptive Randomizations
Liang Jiang, Liyao Li, Ke Miao +1
Our paper discovers a new trade-off of using regression adjustments (RAs) in causal inference under covariate-adaptive randomizations (CARs). On one hand, RAs can improve the effic…
Covariate Adjustment in Experiments with Matched Pairs
Yuehao Bai, Liang Jiang, Joseph P. Romano +2
This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to "matched pairs" and it is additionally desired to a…
Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance
Liang Jiang, Oliver B. Linton, Haihan Tang +1
We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-…
Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations
Liang Jiang, Peter C. B. Phillips, Yubo Tao +1
Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra covariates in addition to the strata indicators. We propose to incorporate these…
Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs
Liang Jiang, Xiaobin Liu, Peter C. B. Phillips +1
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inf…