mlsanityData to 14 Oct 2026

Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis

arXiv 2410.16870 ↗First posted 22 Oct 2024stat.ML cs.LG math.STcited by 3

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Abstract (arXiv)

We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-analysis to one-shot and multi-shot federated learning, the latter leveraging the full data to learn the outcome model (albeit requiring more communication). Focusing on Randomized Controlled Trials (RCTs), we derive the asymptotic variance of these estimators for linear models. Our results provide practical guidance on selecting the appropriate estimator for various scenarios, including heterogeneity in sample sizes, covariate distributions, treatment assignment schemes, and center effects. We validate these findings with a simulation study.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 22 Oct 2024.

Builds on

17 of 51 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  4. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 367
  5. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  6. 2021Federated Causal Inference in Heterogeneous Observational Datacited by 24
  7. 2023An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscited by 19
  8. 2021Federated Adaptive Causal Estimation (FACE) of Target Treatment Effectscited by 16
  9. 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
  10. 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effectscited by 13
  11. 2019Doubly robust treatment effect estimation with missing attributescited by 12
  12. 2021Federated Multi-View Learning for Private Medical Data Integration and Analysiscited by 12
5 more
  1. 2023Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneitycited by 10
  2. 2023Differentially Private Multi-Site Treatment Effect Estimationcited by 4
  3. 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysiscited by 4
  4. 2024A New Theoretical Perspective on Data Heterogeneity in Federated Optimizationcited by 4
  5. 2023FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settingscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregationcited by 2
  2. 2026Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluationcited by 0
  3. 2026A Survey on Federated Causal Discovery and Inferencecited by 0