Personalized Care Through Causal & Federated Learning: From Data to Decisions

Generalization methods offer a powerful solution to one of the key drawbacks of randomized controlled trials (RCTs): their limited representativeness. By enabling the transport of treatment effect estimates to target populations subject to distributional shifts, these methods are increasingly recognized as the future of meta-analysis, the current gold standard in evidence-based medicine. Yet most existing approaches focus on the risk difference, overlooking the diverse range of causal measures routinely reported in clinical research.

To address this gap, we propose a unified framework for transporting a broad class of first-moment population causal effect measures under covariate shift. We will then address scenarios where multiple clinical trials and real world data are available and explore how causal federated learning can be used to aggregate evidence across these sources.

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Institute of AI in Management
LMU Munich