mlsanityData to 7 Oct 2026

Federated Causal Inference in Heterogeneous Observational Data

arXiv 2107.11732 ↗First posted 25 Jul 2021cs.LG econ.EM q-bio.QMcited by 24

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Abstract (arXiv)

We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual-level data cannot be shared across sites; the sites may also have heterogeneous populations and treatment assignment mechanisms. Motivated by these considerations, we develop federated methods to draw inference on the average treatment effects of combined data across sites. Our methods first compute summary statistics locally using propensity scores and then aggregate these statistics across sites to obtain point and variance estimators of average treatment effects. We show that these estimators are consistent and asymptotically normal. To achieve these asymptotic properties, we find that the aggregation schemes need to account for the heterogeneity in treatment assignments and in outcomes across sites. We demonstrate the validity of our federated methods through a comparative study of two large medical claims databases.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 15 of its 88 references that are in the map. First posted on arXiv 25 Jul 2021.

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15 of 88 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  5. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  6. 2015An Introduction to Convolutional Neural Networkscited by 510
  7. 2018Anchor regression: heterogeneous data meets causalitycited by 116
  8. 2017Double/Debiased/Neyman Machine Learning of Treatment Effectscited by 76
  9. 2016Efficient Distributed Learning with Sparsitycited by 38
  10. 2020Combining Observational and Experimental Datasets Using Shrinkage Estimatorscited by 28
  11. 2020Using Experiments to Correct for Selection in Observational Studiescited by 28
  12. 2019Heterogeneity-aware and communication-efficient distributed statistical inferencecited by 15
3 more
  1. 2018Propensity Score Methods for Merging Observational and Experimental Datasetscited by 9
  2. 2021Federated Estimation of Causal Effects from Observational Datacited by 4
  3. 2017Providing Accurate Models across Private Partitioned Data: Secure Maximum Likelihood Estimationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 6; 2025: 5; 2026: 4.

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  1. 2023An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscited by 19
  2. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  3. 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effectscited by 13
  4. 2022Towards Privacy-Aware Causal Structure Learning in Federated Settingcited by 11
  5. 2023Federated Causality Learning with Explainable Adaptive Optimizationcited by 11
  6. 2024Multi-Source Conformal Inference Under Distribution Shiftcited by 11
  7. 2022Collaborative causal inference on distributed datacited by 10
  8. 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysiscited by 4
  9. 2023Federated Causal Inference from Observational Datacited by 3
  10. 2024Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysiscited by 3
  11. 2023FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settingscited by 2
  12. 2024Federated Learning for Estimating Heterogeneous Treatment Effectscited by 2
12 more
  1. 2025Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregationcited by 2
  2. 2024Estimation of conditional average treatment effects on distributed confidential datacited by 1
  3. 2025Federated Instrumental Variable Analysis via Federated Generalized Method of Momentscited by 1
  4. 2023Federated Offline Policy Learningcited by 0
  5. 2024Data-light Uncertainty Set Merging with Admissibilitycited by 0
  6. 2025Causal Policy Learning in Reinforcement Learning: Backdoor-Adjusted Soft Actor-Criticcited by 0
  7. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  8. 2025Latent Factor Point Processes for Patient Representation in Electronic Health Recordscited by 0
  9. 2026Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluationcited by 0
  10. 2026A Survey on Federated Causal Discovery and Inferencecited by 0
  11. 2026Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcomecited by 0
  12. 2026An Accurate and Single-Communication Federated Inference Algorithmcited by 0