arXiv：cs.LG· Laura Fuentes-Vicente, Mathieu Even, Ga\"elle Dormion, Antoine Chambaz, Uri Shalit, Julie Josse·· 4 小时前AI 评分31
Set-Valued Policy Learning：面向分类治疗的集合值策略学习框架
Set-Valued Policy Learning
AI 导读
研究者提出集合值策略学习范式，输出一组有价值的治疗方案，其集合大小反映推荐的不确定性，以支持临床决策。为此定义了基于选择函数的集合策略价值并开发双重稳健估计器，同时提出 Greatest Lower Bound 方法和 conformal 集合值策略学习两种互补方法。在合成数据及创伤护理和体外受精（IVF）真实应用中，该方法产出的策略兼顾性能与可靠性。
正文
Abstract:Conventional treatment policies map patient covariates to a single recommended intervention in order to maximize expected clinical outcomes. However, when multiple treatments yield statistically indistinguishable outcomes or when treatment has no effect, recommending a single intervention may result in somewhat arbitrary interventions, undermining clinical adoption and trust. To address this, we propose a set-valued policy learning paradigm. By outputting sets of valuable treatments whose cardinality reflects the recommendation's ambiguity, our approach better supports clinical decision-making. Evaluating a set-valued policy proves subtle due to the range of possible downstream decisions. To do so, we define the set-policy value using a choice function to model clinical decision-making, and we develop doubly robust estimators thereof. Despite its practical importance, set-valued policy learning for categorical treatments remains largely unexplored. In this context, we introduce two complementary approaches: the Greatest Lower Bound method, which extends the learning-to-defer framework to multiple treatments, and conformal set-valued policy learning, which bridges the gap between unobserved ground-truth optimal treatments and estimated optimal treatment rules. Through experiments on synthetic data and real-world applications to trauma care and 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.
| Subjects: | Machine Learning (cs.LG); Statistics Theory (math.ST) |
| Cite as: | arXiv:2605.19830 [cs.LG] |
| (or arXiv:2605.19830v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.19830 arXiv-issued DOI via DataCite |
Submission history
From: Laura Fuentes-Vicente [view email]
[v1]
Tue, 19 May 2026 13:24:26 UTC (2,056 KB)
[v2]
Wed, 7 Oct 2026 15:59:21 UTC (1,886 KB)
来源：arXiv：cs.LG · arxiv.org