Federated Causal Inference in Healthcare: Methods, Challenges, and Applications
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 4 May 2025.
Builds on
17 of 36 references.
References · 17
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- 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
- 2021Federated Learning for Open Bankingcited by 141
- 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
- 2021OOD-GNN: Out-of-Distribution Generalized Graph Neural Networkcited by 78
- 2021Deep Federated Learning for Autonomous Drivingcited by 54
- 2023An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscited by 19
- 2024Spectral Invariant Learning for Dynamic Graphs under Distribution Shiftscited by 19
- 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
- 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effectscited by 13
- 2023Federated Causality Learning with Explainable Adaptive Optimizationcited by 11
- 2023Federated Recommendation with Additive Personalizationcited by 9
5 more
- 2023Heterogeneous Datasets for Federated Survival Analysis Simulationcited by 5
- 2024Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Modelscited by 4
- 2024Federated Learning for Estimating Heterogeneous Treatment Effectscited by 2
- 2024Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Datacited by 2
- 2024Bayesian Federated Inference for Survival Modelscited by 2
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Citing papers per year: 2026: 1.
1 paper.