Individualized treatment regimes (ITRs) aim to improve clinical outcomes by assigning treatment based on patient-specific characteristics. However, existing methods often struggle with high-dimensional covariates, limiting accuracy, interpretability, and real-world applicability. We propose a novel sufficient dimension reduction approach that directly targets the contrast between potential outcomes and identifies a low-dimensional subspace of the covariates capturing treatment effect heterogeneity. This reduced representation enables more accurate estimation of optimal ITRs through outcome-weighted learning. To accommodate observational data, our method incorporates kernel-based covariate balancing, allowing treatment assignment to depend on the full covariate set and avoiding the restrictive assumption that the subspace sufficient for modeling heterogeneous treatment effects is also sufficient for confounding adjustment. We show that the proposed method achieves universal consistency, i.e., its risk converges to the Bayes risk, under mild regularity conditions. We demonstrate its finite sample performance through simulations and an analysis of intensive care unit sepsis patient data to determine who should receive transthoracic echocardiography.

翻译：个体化治疗策略旨在通过基于患者特异性特征分配治疗来改善临床结果。然而，现有方法常难以处理高维协变量，限制了准确性、可解释性和实际应用性。我们提出了一种新颖的充分降维方法，该方法直接针对潜在结果间的对比，并识别出一个捕捉治疗效果异质性的低维协变量子空间。这种降维表示通过结果加权学习实现了对最优个体化治疗策略的更准确估计。为适应观察性数据，我们的方法结合了基于核的协变量平衡，允许治疗分配依赖于完整的协变量集，并避免了以下限制性假设：即对建模异质治疗效果充分的子空间对混杂调整也是充分的。我们证明，在温和的正则性条件下，所提方法实现了普遍一致性，即其风险收敛于贝叶斯风险。我们通过模拟和对重症监护室脓毒症患者数据的分析来评估其有限样本性能，以确定哪些患者应接受经胸超声心动图检查。