5 papers
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…
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…
Matching-Based Nonparametric Estimation of Group Average Treatment Effects
Peng Wu, Pengtao Zeng, Zhaoqing Tian +1
Heterogeneous treatment effects, which vary according to individual covariates, are crucial in fields such as personalized medicine and tailored treatment strategies. In many appli…
Optimal Policy Adaptation under Covariate Shift
Xueqing Liu, Qinwei Yang, Zhaoqing Tian +2
Transfer learning of prediction models has been extensively studied, while the corresponding policy learning approaches are rarely discussed. In this paper, we propose principled a…
Semiparametric Efficient Inference for the Probability of Necessary and Sufficient Causation
Zhaoqing Tian, Peng Wu
Causal attribution, which aims to explain why events or behaviors occur, is crucial in causal inference and enhances our understanding of cause-and-effect relationships in scientif…