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Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment by Marjan Meurisse; Francisco Estupiñán-Romero; Javier González-Galindo; Natalia Martínez-Lizaga; Santiago Royo-Sierra; Simon Saldner; Lorenz Dolanski-Aghamanoukjan; Alexander Degelsegger-Marquez; Stian Soiland-Reyes; Nina Van Goethem; Enrique Bernal-Delgado; for the BeYond-COVID project is a Mathematics article available to read on EtoBox.

What is Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment about?

## Introduction Causal inference helps researchers and policy-makers to evaluate public health interventions. When comparing interventions or public health programs by leveraging observational sensitive individual-level data from populations crossing jurisdictional borders, a federated approach (as opposed to a pooling data approach) can be used. Approaching causal inference by re-using routinely collected observational data across different regions in a federated manner, is challenging and guidance is currently lacking. With the aim of filling this gap and allowing a rapid response in the case of a next pandemic, a methodological framework to develop studies attempting causal inference using federated cross-national sensitive observational data, is described and showcased within the European BeYond-COVID project. ## Methods A framework for approaching federated causal inference by re-using routinely collected observational data across different regions, based on principles of legal, organizational, semantic and technical interoperability, is proposed. The framework includes step-by-step guidance, from defining a research question, to establishing a causal model, identifying and sp

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Author

Marjan Meurisse; Francisco Estupiñán-Romero; Javier González-Galindo; Natalia Martínez-Lizaga; Santiago Royo-Sierra; Simon Saldner; Lorenz Dolanski-Aghamanoukjan; Alexander Degelsegger-Marquez; Stian Soiland-Reyes; Nina Van Goethem; Enrique Bernal-Delgado; for the BeYond-COVID project

Publisher

Springer Science and Business Media LLC

Published

2023

Field

Mathematics (Physical Sciences)