aiqbench analysis
This paper advances causal inference by introducing set-valued policy learning, which outputs multiple clinically equivalent treatment options rather than a single arbitrary recommendation when outcomes are indistinguishable. By developing doubly robust estimators and choice functions, the authors provide a rigorous framework for handling ambiguity in categorical treatments across domains like trauma care and IVF. This work addresses a critical gap in decision-making reliability where traditional point-estimate policies may undermine trust due to their inherent arbitrariness.
Set-Valued Policy Learning
arXiv:2605.19830v2 Announce Type: replace 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.
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.
Machine Learning (cs.LG); Statistics Theory (math.ST)
arXiv:2605.19830 [cs.LG]
(or arXiv:2605.19830v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.19830 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 296 words
Related coverage
The Entangled Dimensions of Decision Theory
LessWrong · Jul 30, 2026
CAST: Alternating State-Value Targets and Expanded Policy Gradients for Model-Based Reinforcement Learning
arXiv · Sep 9, 2026
Structure-Conditioned Actor-Critic Branches for Quality-Diversity Reinforcement Learning
arXiv · Jun 9, 2026
Horizon Adaptive Offline Policy Learning via Value Stitching
arXiv · Oct 14, 2026