标题： 一种新的针对联邦学习框架用于估计治疗效果的异质性：在老年队列中的应用的稳健框架 显示英文标题

标题： A New Targeted-Federated Learning Framework for Estimating Heterogeneity of Treatment Effects: A Robust Framework with Applications in Aging Cohorts

摘要： 从多个来源分析数据为提高因果估计量的估计效率提供了有价值的机会。 然而，由于人群异质性和数据隐私限制，这种分析也带来了许多挑战。 尽管近年来已开发出几种用于联邦环境下的因果推断先进方法，但许多方法侧重于基于差异的平均因果效应，并未设计用于研究效应修饰。 在本研究中，我们引入了一种新的目标联邦学习框架，通过提出一种基于投影的估计量来研究目标人群的处理效应异质性（HTE）。 该HTE框架在不共享原始数据的情况下整合来自多个数据源的信息，同时考虑各源之间的协变量分布变化。 我们提出的方法被证明是双重稳健的，方便地支持连续结果的基于差异的估计量和二元结果的基于比值的估计量。 此外，我们开发了一种通信高效的基于引导的筛选程序以检测不可运输的数据源，从而在不引入偏差的情况下增强稳健的信息聚合。 通过广泛的模拟研究，展示了所提出的估计量相对于现有方法的优越性能，并且我们的方法在使用全国范围的Medicare关联数据的现实世界数据应用中显示了其有效性。 显示英文摘要

摘要： Analyzing data from multiple sources offers valuable opportunities to improve the estimation efficiency of causal estimands. However, this analysis also poses many challenges due to population heterogeneity and data privacy constraints. While several advanced methods for causal inference in federated settings have been developed in recent years, many focus on difference-based averaged causal effects and are not designed to study effect modification. In this study, we introduce a novel targeted-federated learning framework to study the heterogeneity of treatment effects (HTEs) for a targeted population by proposing a projection-based estimand. This HTE framework integrates information from multiple data sources without sharing raw data, while accounting for covariate distribution shifts among sources. Our proposed approach is shown to be doubly robust, conveniently supporting both difference-based estimands for continuous outcomes and odds ratio-based estimands for binary outcomes. Furthermore, we develop a communication-efficient bootstrap-based selection procedure to detect non-transportable data sources, thereby enhancing robust information aggregation without introducing bias. The superior performance of the proposed estimator over existing methods is demonstrated through extensive simulation studies, and the utility of our approach has been shown in a real-world data application using nationwide Medicare-linked data.