Learning individualized treatment rules (ITRs) is an important topic in precision medicine. Current literature mainly focuses on deriving ITRs from a single source population. We consider the observational data setting when the source population differs from a target population of interest. Compared with causal generalization for the average treatment effect which is a scalar quantity, ITR generalization poses new challenges due to the need to model and generalize the rules based on a prespecified class of functions which may not contain the unrestricted true optimal ITR. The aim of this paper is to develop a weighting framework to mitigate the impact of such misspecification and thus facilitate the generalizability of optimal ITRs from a source population to a target population. Our method seeks covariate balance over a non-parametric function class characterized by a reproducing kernel Hilbert space and can improve many ITR learning methods that rely on weights. We show that the proposed method encompasses importance weights and overlap weights as two extreme cases, allowing for a better bias-variance trade-off in between. Numerical examples demonstrate that the use of our weighting method can greatly improve ITR estimation for the target population compared with other weighting methods.

翻译：学习个体化治疗规则（ITR）是精准医学中的重要课题。现有文献主要关注从单一源人群推导ITR。本文考虑观察性数据情境，其中源人群与感兴趣的目标人群存在差异。与针对平均处理效应（标量）的因果泛化相比，ITR泛化面临新的挑战，因为需要基于预指定的函数类对规则进行建模与泛化，而该函数类可能不包含无约束的真实最优ITR。本文旨在发展一种加权框架，以减轻此类错误设定的影响，从而促进最优ITR从源人群到目标人群的可推广性。我们的方法通过在再生核希尔伯特空间界定的非参数函数类上寻求协变量平衡，可改进许多依赖权重的ITR学习方法。理论证明表明，所提方法将重要性权重与重叠权重作为两种极端情形包含在内，从而允许在两者之间实现更好的偏差-方差权衡。数值例子表明，与其他加权方法相比，使用我们的加权方法可显著改善目标人群的ITR估计。