Personalized decision-making, aiming to derive optimal treatment regimes based on individual characteristics, has recently attracted increasing attention in many fields, such as medicine, social services, and economics. Current literature mainly focuses on estimating treatment regimes from a single source population. In real-world applications, the distribution of a target population can be different from that of the source population. Therefore, treatment regimes learned by existing methods may not generalize well to the target population. Due to privacy concerns and other practical issues, individual-level data from the target population is often not available, which makes treatment regime learning more challenging. We consider the problem of treatment regime estimation when the source and target populations may be heterogeneous, individual-level data is available from the source population, and only the summary information of covariates, such as moments, is accessible from the target population. We develop a weighting framework that tailors a treatment regime for a given target population by leveraging the available summary statistics. Specifically, we propose a calibrated augmented inverse probability weighted estimator of the value function for the target population and estimate an optimal treatment regime by maximizing this estimator within a class of pre-specified regimes. We show that the proposed calibrated estimator is consistent and asymptotically normal even with flexible semi/nonparametric models for nuisance function approximation, and the variance of the value estimator can be consistently estimated. We demonstrate the empirical performance of the proposed method using simulation studies and a real application to an eICU dataset as the source sample and a MIMIC-III dataset as the target sample.

翻译：个性化决策旨在根据个体特征推导最优治疗方案，近年来在医学、社会服务及经济学等多个领域受到日益关注。现有文献主要集中于从单一源群体中估计治疗方案。然而在实际应用中，目标群体的分布可能与源群体存在差异，因此现有方法学习的治疗方案难以有效推广至目标群体。受隐私保护及其他现实问题制约，目标群体的个体层面数据往往无法获取，这进一步增加了治疗方案学习的难度。本研究探讨当源群体与目标群体存在异质性、源群体可获得个体层面数据、而目标群体仅能获取协变量汇总信息（如矩）时的治疗方案估计问题。我们构建了一个加权框架，通过利用可获得的汇总统计量为特定目标群体定制治疗方案。具体而言，针对目标群体的价值函数，我们提出了经校准的增广逆概率加权估计量，并通过在预设治疗方案类别中最大化该估计量来估计最优治疗方案。研究表明，即使采用灵活的半参数/非参数模型逼近干扰函数，所提出的校准估计量仍具有一致性及渐近正态性，且价值估计量的方差可被一致估计。我们通过模拟研究及实际应用（以eICU数据集为源样本、MIMIC-III数据集为目标样本）验证了所提方法的实证性能。