stat.ME · 2026-06-18 · No. 27

Methodology, 2026-06-18.

1 new papers in stat.ME. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

1 entries
  1. 01

    Wasserstein Policy Learning for Distributional Outcomes

    Yiyan Huang, Cheuk Hang Leung, Qi Wu, Zhiheng Zhang

    stat.ME · cs.LG · econ.EM · stat.ML

    Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates to treatment that maximizes the empirical welfare defined as the mean of scalar-valued potential outcomes. In this paper, we study offline policy learning with distribution-valued outcomes, where each potential outcome is a probability measure on $\mathbb{R}$ and...

    arxiv.org/abs/2606.19117 · PDF

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