Federated difference-in-differences with multiple time periods in DataSHIELD

Huth, Manuel, Garavito, Carolina Alvarez, Seep, Lea, Cirera, Laia, Saúte, Francisco, Sicuri, Elisa ORCID: 0000-0002-2499-2732 and Hasenauer, Jan (2024) Federated difference-in-differences with multiple time periods in DataSHIELD. iScience, 27 (11). ISSN 2589-0042

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Abstract

Difference-in-differences (DID) is a key tool for causal impact evaluation but faces challenges when applied to sensitive data restricted by privacy regulations. Obtaining consent can shrink sample sizes and reduce statistical power, limiting the analysis’s effectiveness. Federated learning addresses these issues by sharing aggregated statistics rather than individual data, though advanced federated DID software is limited. We developed a federated version of the Callaway and Sant’Anna difference-in-differences (CSDID), integrated into the DataSHIELD platform, adhering to stringent privacy protocols. Our approach reproduces key estimates and standard errors while preserving confidentiality. Using simulated and real-world data from a malaria intervention in Mozambique, we demonstrate that federated estimates increase sample sizes, reduce estimation uncertainty, and enable analyses when data owners cannot share treated or untreated group data. Our work contributes to facilitating the evaluation of policy interventions or treatments across centers and borders.

Item Type:	Article
Additional Information:	© 2024 The Author(s)
Divisions:	LSE Health
Subjects:	R Medicine > RA Public aspects of medicine
Date Deposited:	11 Nov 2024 08:03
Last Modified:	02 Dec 2024 19:54
URI:	http://eprints.lse.ac.uk/id/eprint/125985

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