Set-Valued Policy Learning
集合值政策学习AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要围绕治疗效果驱动的多治疗政策学习及最优治疗规则的不确定性展开。
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集合值政策学习
- 研究问题
- 在多种治疗可选时，如何用一组合理治疗表达决策不确定性，减少单一推荐对估计误差、模型设定和有限样本波动的敏感性？
- 方法与思路
- 作者以最大下界方法扩展多治疗延迟决策，结合保形政策学习与随机性注入，将治疗集合的大小用于表达决策歧义。
- 材料陈述的贡献
- 摘要称，随机性注入无需约束黑箱最优治疗规则即可保证边际覆盖，并以合成数据及体外受精应用展示政策表现与可靠性的平衡。
- 阅读关注点
- 应核对覆盖的具体对象、随机性注入机制、估计最优治疗与真实最优治疗的关系，以及集合大小和政策价值之间的权衡。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明覆盖水平与其他成立条件，边际覆盖不能直接解释为个体覆盖保证。
原文摘要
Conventional treatment policies map patient covariates to a single recommended intervention in order to maximize expected clinical outcomes. Although a rich body of causal inference methods has been developed to estimate such policies, point-valued recommendations can be highly sensitive to estimation uncertainty, model specification, and finite-sample variability, while typically providing little guidance about how confident one should be in the recommended action. In this work, we propose a set-valued policy learning paradigm for the multiple-treatment setting, in which policies output a set of plausible treatments rather than a single recommendation. This formulation enables intrinsic uncertainty quantification, with the size of the predicted set reflecting the degree of decision ambiguity. We extend the learning-to-defer framework to multiple treatments via a novel \textit{greatest Lower Bound} method, and introduce \textit{conformal policy learning}, which bridges the gap between unobserved ground-truth optimal treatments and estimated optimal treatment rules. Drawing on insights from the noisy-label literature, we develop a randomness-injection approach that guarantees marginal coverage without requiring assumptions on underlying black-box optimal treatment rules. Through experiments on synthetic data and a real-world application to In-Vitro Fertilization (IVF), we demonstrate that our methods produce robust and actionable policies that naturally incorporate clinical considerations while effectively balancing performance and reliability.