Causal inference typically assumes centralized access to individual-level data. Yet, in practice, data are often decentralized across multiple sites, making centralization infeasible due to privacy, logistical, or legal constraints. We address this problem by estimating the Average Treatment Effect (ATE) from decentralized observational data via a Federated Learning (FL) approach, allowing inference through the exchange of aggregate statistics rather than individual-level data. We propose a novel method to estimate propensity scores via a federated weighted average of local scores using Membership Weights (MW), defined as probabilities of site membership conditional on covariates. MW can be flexibly estimated with parametric or non-parametric classification models using standard FL algorithms. The resulting propensity scores are used to construct Federated Inverse Propensity Weighting (Fed-IPW) and Augmented IPW (Fed-AIPW) estimators. In contrast to meta-analysis methods, which fail when any site violates positivity, our approach exploits heterogeneity in treatment assignment across sites to improve overlap. We show that Fed-IPW and Fed-AIPW perform well under site-level heterogeneity in sample sizes, treatment mechanisms, and covariate distributions. Theoretical analysis and experiments on simulated and real-world data demonstrate clear advantages over meta-analysis and related approaches.

翻译：传统因果推断通常假设能够集中访问个体层面的数据。然而在实践中，数据往往分散存储于多个站点，由于隐私、管理或法律限制而无法集中处理。本文通过联邦学习（FL）方法，利用分散的观测数据估计平均处理效应（ATE），该方法仅需交换聚合统计量而非个体层面数据即可完成推断。我们提出一种新颖的方法，通过使用成员权重（MW）对局部倾向得分进行联邦加权平均来估计倾向得分，其中成员权重定义为给定协变量条件下站点归属的概率。MW可通过参数化或非参数化分类模型，利用标准FL算法灵活估计。所得倾向得分用于构建联邦逆倾向加权（Fed-IPW）与增强逆倾向加权（Fed-AIPW）估计量。与元分析方法相比（当任意站点违反正性假设时该方法即失效），我们的方法能够利用不同站点间处理分配的异质性来改善重叠性。研究表明，Fed-IPW和Fed-AIPW在样本量、处理机制和协变量分布存在站点层面异质性的情况下均表现良好。理论分析及在模拟与真实数据上的实验表明，本方法较元分析及相关方法具有显著优势。