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stat.ME2025
Covariate Adjustment Cannot Hurt: Treatment Effect Estimation under Interference with Low-Order Outcome Interactions
Xinyi Wang, Shuangning Li
In randomized experiments, covariates are often used to reduce variance and improve the precision of treatment effect estimates. However, in many real-world settings, interference…
stat.ML2025
Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
Shuangning Li, Chonghuan Wang, Jingyan Wang
We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has observed that the stable unit treatment value assumption (SUT…
stat.ME2025
Optimal Targeting in Dynamic Systems
Yuchen Hu, Shuangning Li, Stefan Wager
Modern treatment targeting methods often rely on estimating a conditional average treatment effect (CATE) using machine learning tools. While effective in identifying who benefits…