5 papers
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…
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…
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…