2 citations · 3 across the 3 of their papers we have counts for
6 papers
Tyranny-of-the-minority regression adjustment in randomized experiments
Xin Lu, Hanzhong Liu
Regression adjustment is widely used for the analysis of randomized experiments to improve the estimation efficiency of the treatment effect. This paper reexamines a weighted regre…
Rerandomization and covariate adjustment in split-plot designs
Wenqi Shi, Anqi Zhao, Hanzhong Liu
The split-plot design arises from agricultural sciences with experimental units, also known as subplots, nested within groups known as whole plots. It assigns the whole-plot interv…
Rerandomization in stratified randomized experiments
Xinhe Wang, Tingyu Wang, Hanzhong Liu
Stratification and rerandomization are two well-known methods used in randomized experiments for balancing the baseline covariates. Renowned scholars in experimental design have re…
Confidence intervals for parameters in high-dimensional sparse vector autoregression
Ke Zhu, Hanzhong Liu
Vector autoregression (VAR) models are widely used to analyze the interrelationship between multiple variables over time. Estimation and inference for the transition matrices of VA…
Heterogeneous Treatment Effect Estimation through Deep Learning
Ran Chen, Hanzhong Liu
Estimating heterogeneous treatment effect is an important task in causal inference with wide application fields. It has also attracted increasing attention from machine learning co…
Penalized regression adjusted causal effect estimates in high dimensional randomized experiments
Hanzhong Liu, Yuehan Yang
Regression adjustments are often considered by investigators to improve the estimation efficiency of causal effect in randomized experiments when there exists many pre-experiment c…