Heterogeneity-Aware Federated Causal Inference Leveraging Effect-Measure Transportability
利用效应度量可迁移性的异质性感知联邦因果推断
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…