Estimating individualized treatment rules (ITRs) is crucial for tailoring interventions in precision medicine. Typical ITR estimation methods rely on conditional average treatment effects (CATEs) to guide treatment assignments. However, such methods overlook individual-level harm within covariate-specific subpopulations, potentially leading many individuals to experience worse outcomes under CATE-based ITRs. In this article, we aim to estimate ITRs that maximize the reward while ensuring that the harm rate induced by the ITR remains below a pre-specified threshold. We first derive the explicit form of the oracle ITR. However, the oracle ITR is not achievable without strong assumptions, as the harm rate is generally unidentifiable due to its dependence on the joint distribution of potential outcomes. To address this, we propose two strategies for estimating ITRs with a harm rate constraint under partial identification and establish their large-sample properties. By accounting for both reward and harm, our method provides a reliable solution for developing ITRs in high-stakes domains where harm is a critical consideration. Extensive simulations demonstrate the effectiveness of the proposed methods in controlling harm rates. We apply the proposed method to analyze two real-world datasets from a new perspective, assessing the potential reduction in harm rate compared with historical interventions.

翻译：估计个体化治疗规则对于精准医疗中的干预措施定制至关重要。典型的ITR估计方法依赖于条件平均处理效应来指导治疗分配。然而，此类方法忽略了协变量特定亚群内的个体层面伤害，可能导致许多个体在基于CATE的ITR下经历更差的治疗结果。本文旨在估计能最大化奖励同时确保ITR引发的伤害率低于预设阈值的治疗规则。我们首先推导出理想ITR的显式形式。但由于伤害率依赖于潜在结果的联合分布而通常不可识别，若无强假设则无法实现理想ITR。为此，我们提出两种在部分识别框架下估计具有伤害率约束的ITR的策略，并建立其大样本性质。通过同时考虑奖励与伤害，我们的方法为在伤害作为关键考量因素的高风险领域开发ITR提供了可靠解决方案。大量仿真实验证明了所提方法在控制伤害率方面的有效性。我们将所提方法应用于两个真实世界数据集的新视角分析，评估其相较于历史干预措施可能实现的伤害率降低程度。