Federated Causal Inference in Healthcare: Methods, Challenges, and Applications

TL;DR

This paper reviews federated causal inference methods in healthcare, analyzing challenges like data heterogeneity and proposing solutions such as FedProx regularization to improve unbiased treatment effect estimation across multiple sites.

Contribution

It provides a comprehensive classification and theoretical analysis of federated causal inference methods, including extensions for personalized models and time-to-event outcomes.

Findings

FedProx regularization improves bias-variance trade-off

Heterogeneity impacts causal effect estimation accuracy

Extensions enable personalized and time-to-event analyses

Abstract

Federated causal inference enables multi-site treatment effect estimation without sharing individual-level data, offering a privacy-preserving solution for real-world evidence generation. However, data heterogeneity across sites, manifested in differences in covariate, treatment, and outcome, poses significant challenges for unbiased and efficient estimation. In this paper, we present a comprehensive review and theoretical analysis of federated causal effect estimation across both binary/continuous and time-to-event outcomes. We classify existing methods into weight-based strategies and optimization-based frameworks and further discuss extensions including personalized models, peer-to-peer communication, and model decomposition. For time-to-event outcomes, we examine federated Cox and Aalen-Johansen models, deriving asymptotic bias and variance under heterogeneity. Our analysis reveals that FedProx-style regularization achieves near-optimal bias-variance trade-offs compared to naive averaging and meta-analysis. We review related software tools and conclude by outlining opportunities, challenges, and future directions for scalable, fair, and trustworthy federated causal inference in distributed healthcare systems.