In many decision-making problems, the primary outcome is expensive, time-consuming, or difficult to observe, so individualized treatment rules (ITRs) may be instead learned from surrogate endpoints. However, a surrogate that is highly associated with the primary outcome, or even satisfies existing surrogate criteria, may not necessarily induce a treatment rule that performs well on the primary outcome, especially under treatment resource budget constraints. In this paper, we develop a principled framework for evaluating the decision-making value of surrogate endpoints. We introduce three ITR-oriented performance measures: surrogate regret, which assesses the expected loss from using the surrogate-optimal ITR instead of outcome-optimal ITR; surrogate gain, which quantifies the benefit of surrogate-optimal ITRs relative to the no-treatment baseline; and surrogate efficiency, which evaluates improvement over random treatment assignment. We also extend them to budget-constrained settings. We propose augmented inverse probability weighted (AIPW) estimators for these measures and establish their large-sample properties. We demonstrate the proposed approach on both simulations and an application to the Criteo dataset.

翻译：在许多决策问题中，主要结局的观测往往代价高昂、耗时漫长或存在困难，因此研究者常基于替代终点学习个体化治疗规则（ITRs）。然而，一个与主要结局高度相关甚至满足现有替代标准指标的替代指标，未必能推导出对主要结局表现良好的治疗规则，特别是在治疗资源预算约束条件下。本文构建了一个评估替代终点决策价值的规范框架，提出三种面向ITR的性能度量：替代遗憾（衡量使用替代最优ITR替代结局最优ITR的期望损失）、替代增益（量化替代最优ITR相较于无治疗基线的效益）以及替代效率（评估相较于随机治疗分配的改进效果），并将其扩展至预算约束场景。针对这些度量指标，我们提出增广逆概率加权（AIPW）估计量，并建立了其大样本性质。通过模拟实验和Criteo数据集的应用验证了所提方法的有效性。