Federated causal discovery in medicine: trends, opportunities, and challenges

Exponential growth and continued digitisation have accelerated the adoption of data-driven and evidence-based approaches in medicine. This includes deciphering associations, including potential causal associations, from multivariate observational biomedical data under certain implicit assumptions. Such an evidence approach marks the shift from classical hypothesis testing to discovery and hypothesis generation. Widespread adoption of common data models has especially accelerated collaborative approaches in medicine while transitioning from centralised to federated architectures that facilitate discovery without the explicit sharing of sensitive medical data. This perspective provides an overview of causal discovery from observational data with a focus on federated learning. Specifically, it outlines the trends, opportunities, and challenges of federated causal discovery in medicine. While medical research has traditionally relied on the hierarchy of evidence generated from the evidence pyramid, the ability of federated causal discovery to facilitate evidence generation collaboratively from heterogeneous sources is expected to enhance the generalizability and transportability of findings while addressing sample size considerations—a critical aspect for its successful and widespread adoption in medicine.