Federated Causal Inference in Heterogeneous Observational Data

Ruoxuan Xiong, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T. Vogelstein and Susan Athey
Additional contact information
Ruoxuan Xiong: Emory University
Allison Koenecke: Microsoft Research New England
Michael Powell: Johns Hopkins University
Zhu Shen: Stanford University
Joshua T. Vogelstein: Johns Hopkins University

Research Papers from Stanford University, Graduate School of Business

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.

Date: 2021-08
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Citations: View citations in EconPapers (1)

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https://arxiv.org/abs/2107.11732

Related works:
Working Paper: Federated Causal Inference in Heterogeneous Observational Data (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:3990

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