为决策提供可信赖的最优性概率评估，无需假设数据分布。
Conformalized Decision Risk Assessment
- 基于逆可行域和共形预测，构建决策最优性的概率下界。
- 在有限样本下给出分布无关的保守估计，准确率高且计算高效。
- 适用于各类优化问题，尤其适合不确定环境下需提前决策的场景。
在许多实际决策场景中，决策者必须在不确定性揭晓前做出选择，但现有优化工具很少能量化某个决策在各种可能情景下保持（近似）最优的概率。本文提出CREDO——面向决策优化的共形风险估计框架，一种无需分布假设的方法，用于估算给定决策在不确定性实现下仍为最优的可能性。CREDO通过逆可行域（即决策为最优的输出集合）重构决策风险，并利用条件生成模型生成的共形预测球构建内部近似，从而获得决策最优性的有限样本、分布无关的下界。该方法具有模型无关性，广泛适用于多种优化问题。大量数值实验表明，CREDO在不同优化设置下均能提供准确、高效且可靠的决策最优性评估。
原文摘要 · Abstract (English)
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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