arXiv subjects

Laura Fuentes-Vicente

Publications and source records attributed to Laura Fuentes-Vicente.

2 recordsLinked to original sources

arXiv2026

Set-Valued Policy Learning

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.

cs.LG↗

arXiv2026

Policy learning under constraint: Maximizing a primary outcome while controlling an adverse event

A medical policy aims to support decision-making by mapping patient characteristics to individualized treatment recommendations. Standard approaches typically optimize a single outcome criterion. For example, recommending treatment according to the sign of the Conditional Average Treatment Effect (CATE) maximizes the policy "value" by exploiting treatment effect heterogeneity. This point of view shifts policy learning towards the challenge of learning a reliable CATE estimator. However, in multi-outcome settings, such strategies ignore the risk of adverse events, despite their relevance. PLUC (Policy Learning Under Constraint) addresses this challenges by learning an estimator of the CATE that yields smoothed policies controlling the probability of an adverse event in observational settings. Inspired by insights from EP-learning, PLUC involves the optimization of strongly convex Lagrangian criteria over a convex hull of functions. Its alternating procedure iteratively applies the Frank-Wolfe algorithm to minimize the current criterion, then performs a targeting step that updates the criterion so that its evaluations at previously visited landmarks become targeted estimators of the corresponding theoretical quantities. An R package PLUC-R provides a practical implementation. We illustrate PLUC's performance through a series of numerical experiments.

stat.ME↗