标题： 考虑异质性的联邦因果推断利用效应度量可迁移性 显示英文标题

标题： Heterogeneity-Aware Federated Causal Inference Leveraging Effect-Measure Transportability

摘要： 联邦学习因果估计量提供了一种强大的策略，通过利用多个研究站点的数据来提高估计效率，同时保持隐私。 现有文献主要集中在使用单一数据源的平均处理效应，而我们的工作则针对跨多个来源的更广泛的因果度量。 我们在两种可转移性假设下推导并比较了半参数高效估计量，这些假设对数据似然施加了不同的限制，并展示了效率与稳健性的权衡。 该估计量还允许纳入灵活的机器学习算法来处理干扰函数，同时保持参数收敛速率和名义覆盖率。 为了进一步处理一些源站点违反可转移性的情况，我们提出了一种后联邦加权选择（PFWS）框架，这是一种两步程序，能够自适应地识别兼容的站点，并在渐近情况下达到半参数效率界限。 该框架减轻了加权方法的效率损失以及直接站点选择在有限样本中的不稳定性和计算负担。 通过广泛的模拟和真实数据分析，我们证明了在各种可转移性场景下，我们的PFWS框架相比目标仅分析具有更优的方差效率。 显示英文摘要

摘要： Federated learning of causal estimands offers a powerful strategy to improve estimation efficiency by leveraging data from multiple study sites while preserving privacy. Existing literature has primarily focused on the average treatment effect using single data source, whereas our work addresses a broader class of causal measures across multiple sources. We derive and compare semiparametrically efficient estimators under two transportability assumptions, which impose different restrictions on the data likelihood and illustrate the efficiency-robustness tradeoff. This estimator also permits the incorporation of flexible machine learning algorithms for nuisance functions while maintaining parametric convergence rates and nominal coverage. To further handle scenarios where some source sites violate transportability, we propose a Post-Federated Weighting Selection (PFWS) framework, which is a two-step procedure that adaptively identifies compatible sites and achieves the semiparametric efficiency bound asymptotically. This framework mitigates the efficiency loss of weighting methods and the instability and computational burden of direct site selection in finite samples. Through extensive simulations and real-data analysis, we demonstrate that our PFWS framework achieves superior variance efficiency compared with the target-only analyses across diverse transportability scenarios.