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stat.ML2026
Counterfactually Safe Reinforcement Learning
Jingyi Li, Peng Wu, Chengchun Shi
Reinforcement learning algorithms are generally designed to maximize the expected return across a population. However, a policy that is optimal on average may be suboptimal for cer…
stat.ME2026
Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls
Qinwei Yang, Jingyi Li, Peng Wu +1
Randomized controlled trials (RCTs) often suffer from limited inferential efficiency in estimating treatment effects due to their small sample sizes. In recent years, incorporating…