用于最优个体化治疗规则的正交双残差学习
Orthogonal double residual learning for optimal individualized treatment rules
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中文总结 AI 辅助
提出正交双残差学习（ODRL），该方法为首个通用奈曼正交目标的最优个体化治疗规则直接方法，经模拟和实际研究验证，性能优异且鲁棒性强。
中文摘要 AI 辅助
个体化治疗规则（ITR）将基线特征映射到治疗推荐，最优ITR可最大化期望奖励或策略福利。间接方法可能需要严格的建模假设，而直接方法易受干扰估计误差和有限重叠的影响。我们提出正交双残差学习（ODRL），这是一种两阶段交叉拟合框架，通过使用治疗与结局残差的乘积进行代价敏感分类，直接针对最优ITR。据我们所知，ODRL是首个具有通用奈曼正交目标的直接方法，既不需要严格的建模假设，也不需要逆倾向得分加权。因此，干扰估计误差通过二阶乘积影响遗憾，ODRL在有限重叠下仍保持鲁棒性。该费希尔一致目标适用于通用决策规则筛选器。我们针对VC类（包括线性规则和决策树）建立了相对于贝叶斯分类器的非渐近高概率值函数遗憾界，以及使用支持向量机和深度ReLU神经网络的代理松弛的校准遗憾界。我们进一步表明，通用代理松弛不一定保持正交性，而有界得分hinge学习可以保持正交性。模拟实验在复杂和线性决策边界、有限重叠以及工作模型误设的情况下均表现出优异性能。对右心导管研究和牛津净零实验的应用展示了可解释的治疗或策略推荐。odrlITR R包实现了ODRL。
英文摘要
Individualized treatment rules (ITRs) map baseline characteristics to treatment recommendations, with the optimal ITR maximizing expected reward or policy welfare. Indirect methods may require restrictive modeling assumptions, whereas direct methods can be sensitive to nuisance estimation error and limited overlap. We propose orthogonal double residual learning (ODRL), a two-stage, cross-fitted framework that directly targets the optimal ITR through cost-sensitive classification using the product of treatment and outcome residuals. To our knowledge, ODRL is the first direct method with a universally Neyman orthogonal objective requiring neither restrictive modeling assumptions nor inverse propensity score weighting. Thus, nuisance estimation errors affect regret through a second-order product, and ODRL remains robust under limited overlap. The Fisher consistent objective accommodates general decision rule sieves. We establish nonasymptotic high probability value function regret bounds relative to the Bayes classifier for VC classes, including linear rules and decision trees, and calibrated regret bounds for surrogate relaxations using support vector machines and deep ReLU neural networks. We further show that generic surrogate relaxations need not preserve orthogonality, whereas bounded score hinge learning does. Simulations demonstrate strong performance across complex and linear decision boundaries, limited overlap, and working model misspecification. Applications to the Right Heart Catheterization study and the Oxford Net Zero experiment illustrate interpretable treatment or policy recommendations. The \texttt{odrlITR} R package implements ODRL.
