183 citations · 292 across the 15 of their papers we have counts for
15 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…
Design-based theory for cluster rerandomization
Xin Lu, Tianle Liu, Hanzhong Liu +1
Complete randomization balances covariates on average, but covariate imbalance often exists in finite samples. Rerandomization can ensure covariate balance in the realized experime…
Pair-switching rerandomization
Ke Zhu, Hanzhong Liu
Rerandomization discards assignments with covariates unbalanced in the treatment and control groups to improve estimation and inference efficiency. However, the acceptance-rejectio…
Randomization-based joint central limit theorem and efficient covariate adjustment in stratified factorial experiments
Hanzhong Liu, Jiyang Ren, Yuehan Yang
Randomized block factorial experiments are widely used in industrial engineering, clinical trials, and social science. Researchers often use a linear model and analysis of covarian…
A general theory of regression adjustment for covariate-adaptive randomization: OLS, Lasso, and beyond
Hanzhong Liu, Fuyi Tu, Wei Ma
We consider the problem of estimating and inferring treatment effects in randomized experiments. In practice, stratified randomization, or more generally, covariate-adaptive random…
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…
Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective
Wei Ma, Fuyi Tu, Hanzhong Liu
Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been…
Regression-adjusted average treatment effect estimates in stratified randomized experiments
Hanzhong Liu, Yuehan Yang
Researchers often use linear regression to analyse randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and cont…
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…
A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models
Hanzhong Liu, Xin Xu, Jingyi Jessica Li
Constructing confidence intervals for the coefficients of high-dimensional sparse linear models remains a challenge, mainly because of the complicated limiting distributions of the…
Lasso adjustments of treatment effect estimates in randomized experiments
Adam Bloniarz, Hanzhong Liu, Cun-Hui Zhang +2
We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to an…
Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression
Hanzhong Liu, Bin Yu
We study the asymptotic properties of Lasso+mLS and Lasso+Ridge under the sparse high-dimensional linear regression model: Lasso selecting predictors and then modified Least Square…