Reliable decision making pipelines powered by machine learning models require uncertainty quantification (UQ) methods that come with explicit safety guarantees. Conformal prediction provides such UQ by wrapping ML predictions into prediction sets, and recent work by Kiyani et al. (2025b) established that these sets can be translated into optimal risk-averse decision policies -- yet only inheriting marginal safety guarantees. We generalize and strengthen their results by (i) introducing action-conditional conformal prediction, which yields safety guarantees conditioned explicitly on each action taken by the decision maker, (ii) showing that action-conditional prediction sets serve as a proxy for the feasible decision space for risk-averse decision makers aiming to optimize action-conditional value-at-risk, and (iii) proposing a principled finite-sample algorithm based on pinball-loss minimization, connecting the framework of Gibbs et al. (2025) to action-conditional guarantees. Experiments on two real-world datasets confirm that our approach significantly improves action-conditional performance over conformal baselines.

翻译：摘要：由机器学习模型驱动的可靠决策管道需要带有明确安全保证的不确定性量化（UQ）方法。保形预测通过将机器学习预测结果包裹成预测集来提供此类UQ，且Kiyani等人（2025b）的最新工作表明，这些预测集可以转化为最优风险厌恶决策策略——然而仅继承了边际安全保证。我们通过以下三点推广并强化了他们的结果：(i) 引入动作条件保形预测，该预测生成的保证明确取决于决策者采取的每个动作；(ii) 证明动作条件预测集可作为风险厌恶决策者优化动作条件风险价值时可行决策空间的代理；(iii) 提出基于分位数损失最小化的原理性有限样本算法，将Gibbs等人（2025）的框架与动作条件保证联系起来。在两个真实世界数据集上的实验证实，我们的方法在动作条件性能上显著优于保形基线方法。