发表机构
Graduate School of Information, Yonsei University（延世大学信息研究生院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出预测-优化框架，通过机会约束选择控制累积成本超预算概率，在临床治疗等环境中可在点估计规则超支时维持预算，所有代码公开。
AI 中文摘要
许多操作决策是受累积资源限制的干预序列，例如在 crew-hour（工时）预算内的维护计划。选择这些决策需要了解每个决策产生的结果和累积成本，这些是从观测数据中识别出的反事实量。两个具有相同预期成本的策略超预算的概率可能差异很大，因此仅约束均值无法限制超支发生的频率。此前的两步架构（最近已扩展到连续剂量场景）仅约束平均成本而非其尾部，且在单个决策点进行分配。确实能约束成本尾部的方法，其成本分布来自指定模型而非从数据中识别。本文提出一种预测-优化框架：预测步骤中，任何返回结果值和成本分布的估计器均可为决策规则提供所需输入，因此预测器可互换；优化步骤中，对有限候选集进行机会约束选择，以限制累积成本超过预算的概率。由于该尾部无法跨阶段分解，因此对每个策略整体评分。遍历可容忍的违规概率可得到安全-效用前沿，且无分布的有限样本边界涵盖违规和结果不足。五个环境中，四个涵盖临床治疗和设备维护，提供精确的反事实真实值；第五个来自数字健康微随机试验的真实结果。在所有环境中，该规则在点估计规则超支的情况下仍能控制预算，且结果成本可通过前沿明确体现。所有代码可在 https URL 获取
英文摘要
Many operational decisions are sequences of interventions under a cumulative resource limit, such as a maintenance schedule within a crew-hour budget. Choosing among them calls for the outcome and the cumulative cost each would produce, counterfactual quantities identified from observational data. Two strategies with the same expected cost can exceed the budget at very different rates, so constraining the mean does not bound how often an overrun occurs. Prior two-step architectures, recently extended to continuous doses, constrain the mean cost rather than its tail and allocate at a single decision point. Methods that do bound a cost tail take its distribution from a specified model rather than identifying it from data. We present a predict-then-optimize framework. In the prediction step, any estimator returning an outcome value and a cost distribution supplies what the decision rule consumes, so the predictor is interchangeable. In the optimization step, a chance-constrained selection over a finite candidate set bounds the probability that the cumulative cost exceeds the budget. That tail does not decompose across stages, so each strategy is scored whole. Sweeping the tolerated violation probability traces a safety-utility frontier, and distribution-free finite-sample bounds cover violation and outcome shortfall. Four of five environments, spanning clinical treatment and equipment maintenance, supply exact counterfactual ground truth; the fifth carries real outcomes from a digital-health micro-randomized trial. Across them, the rule holds the budget where a point-estimate rule overruns it, at an outcome cost the frontier makes explicit. All code is available at https://github.com/mfriendly/counterfactual-chance-selection
Comments46 pages, 10 figures, 20 tables. Includes supplementary material as an appendix
