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stat.ML2026
Sparse Additive Off-Policy Evaluation for Reinforcement Learning with Potentially Limited Number of Trajectories
Tuoyi Zhao, Chengchun Shi, Zhengling Qi +1
We develop a new framework for flexible, nonlinear, and interpretable off-policy evaluation for infinite-horizon reinforcement learning. To handle large state spaces and support tr…
stat.ML2026
Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making
Zeyu Bian, Lan Wang, Chengchun Shi +1
Fairness is a central pillar of trustworthy machine learning, especially in domains where accuracy- or profit-driven optimization is insufficient. While most fairness research focu…
stat.ML2026
Beyond Demand Estimation: Consumer Surplus Evaluation via Cumulative Propensity Weights
Zeyu Bian, Max Biggs, Ruijiang Gao +1
This paper develops a practical framework for using observational data to audit the consumer surplus effects of AI-driven decisions, specifically in targeted pricing and algorithmi…