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Federated Causal Inference in Heterogeneous Observational Data

Author

Listed:
  • Xiong, Ruoxuan

    (Emory University)

  • Koenecke, Allison

    (Microsoft Research New England)

  • Powell, Michael

    (Johns Hopkins University)

  • Shen, Zhu

    (Stanford University)

  • Vogelstein, Joshua T.

    (Johns Hopkins University)

  • Athey, Susan

    (Stanford University)

Abstract

Analyzing observational data from multiple sources can be useful for increasing statistical power to detect a treatment effect; however, practical constraints such as privacy considerations may restrict individual-level information sharing across data sets. This paper develops federated methods that only utilize summary-level information from heterogeneous data sets. Our federated methods provide doubly-robust point estimates of treatment effects as well as variance estimates. We derive the asymptotic distributions of our federated estimators, which are shown to be asymptotically equivalent to the corresponding estimators from the combined, individual-level data. We show that to achieve these properties, federated methods should be adjusted based on conditions such as whether models are correctly specified and stable across heterogeneous data sets.

Suggested Citation

  • Xiong, Ruoxuan & Koenecke, Allison & Powell, Michael & Shen, Zhu & Vogelstein, Joshua T. & Athey, Susan, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Research Papers 3990, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3990
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    File URL: https://arxiv.org/abs/2107.11732
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    References listed on IDEAS

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    Cited by:

    1. Aldo Gael Carranza & Susan Athey, 2023. "Federated Offline Policy Learning with Heterogeneous Observational Data," Papers 2305.12407, arXiv.org.

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