Federated causal inference for effect estimation in healthcare: methods, challenges, and applications

Federated causal inference enables treatment-effect estimation across healthcare systems without centralizing patient-level data. We review methods for binary, continuous, and time-to-event outcomes through three lenses: estimand, distributed-computation protocol, and privacy mechanism. We compare major strategies, clarify when federated analyses can approximate pooled results, and highlight unresolved challenges in confounding, heterogeneity, survival estimands, privacy–utility trade-offs, validation, and software. This framework supports rigorous evaluation of federated causal methods in healthcare.