Conformalized Decision Risk Assessment

Wenbin Zhou; Agni Orfanoudaki; Shixiang Zhu

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arXiv · 2505.13243

Conformalized Decision Risk Assessment

Wenbin Zhou·Agni Orfanoudaki·Shixiang Zhu

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Abstract

In many operational settings, decision-makers must commit to actions before uncertainty resolves, but existing optimization tools rarely quantify how consistently a chosen decision remains optimal across plausible scenarios. This paper introduces CREDO -- Conformalized Risk Estimation for Decision Optimization, a distribution-free framework that quantifies the probability that a prescribed decision remains (near-)optimal across realizations of uncertainty. CREDO reformulates decision risk through the inverse feasible region -- the set of outcomes under which a decision is optimal -- and estimates its probability using inner approximations constructed from conformal prediction balls generated by a conditional generative model. This approach yields finite-sample, distribution-free lower bounds on the probability of decision optimality. The framework is model-agnostic and broadly applicable across a wide range of optimization problems. Extensive numerical experiments demonstrate that CREDO provides accurate, efficient, and reliable evaluations of decision optimality across various optimization settings.

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Wenbin Zhou, Agni Orfanoudaki, Shixiang Zhu. 2025-05-19. Conformalized Decision Risk Assessment. https://arxiv.org/abs/2505.13243

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