mlsanityData to 14 Oct 2026

Conformalized Decision Risk Assessment

arXiv 2505.13243 ↗First posted 19 May 2025stat.ML cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 46 of its 88 references that are in the map. First posted on arXiv 19 May 2025.

Builds on

46 of 88 references.

References · 46

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34 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026Conformal Policy Controlcited by 6
  2. 2025Calibrating Decision Robustness via Inverse Conformal Risk Controlcited by 4
  3. 2026Learning Polyhedral Conformal Sets for Robust Optimizationcited by 1