3 citations · 4 across the 8 of their papers we have counts for
8 papers
Unbiased Treatment Effect Estimation under Network Interference via Neighborhood-Excluded Cross-Fitting
Haoyang Yu, Anqi Zhao, Hanzhong Liu
Without interference, cross-fitting enables flexible covariate adjustment while preserving finite-sample unbiasedness under independent unit-level randomization. Under network inte…
Design-based edge-level causal inference with machine learning assisted covariate adjustment
Haoyang Yu, Yilin Li, Lu Deng +3
We study design-based causal inference for edge-level outcomes in directed networks under dyadic interference. In this setting, outcomes are defined on directed edges and depend on…
Design-based theory for causal inference
Xin Lu, Wanjia Fu, Hongzi Li +4
Causal inference, as a major research area in statistics and data science, plays a central role across diverse fields such as medicine, economics, education, and the social science…
Conditional cross-fitting for unbiased machine-learning-assisted covariate adjustment in randomized experiments
Xin Lu, Lei Shi, Hanzhong Liu +1
Randomized experiments are the gold standard for estimating the average treatment effect (ATE). While covariate adjustment can reduce the asymptotic variances of the unbiased Horvi…
Rejoinder to Reader Reaction "On exact randomization-based covariate-adjusted confidence intervals" by Jacob Fiksel
Ke Zhu, Hanzhong Liu
We applaud Fiksel (2024) for their valuable contributions to randomization-based inference, particularly their work on inverting the Fisher randomization test (FRT) to construct co…
Imputation-based randomization tests for randomized experiments with interference
Tingxuan Han, Ke Zhu, Hanzhong Liu +1
The presence of interference renders classic Fisher randomization tests infeasible due to nuisance unknowns. To address this issue, we propose imputing the nuisance unknowns and co…
Sharp variance estimator and causal bootstrap in stratified randomized experiments
Haoyang Yu, Ke Zhu, Hanzhong Liu
Randomized experiments are the gold standard for estimating treatment effects, and randomization serves as a reasoned basis for inference. In widely used stratified randomized expe…
Design-based theory for Lasso adjustment in randomized block experiments and rerandomized experiments
Ke Zhu, Hanzhong Liu, Yuehan Yang
Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regres…