1 citations · 1 across the 5 of their papers we have counts for
5 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…
Minimax Optimal Design with Spillover and Carryover Effects
Haoyang Yu, Wei Ma, Hanzhong Liu
In various applications, the potential outcome of a unit may be influenced by the treatments received by other units, a phenomenon known as interference, as well as by prior treatm…
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…