Federated Causal Discovery From Interventions
Preprint 2022
We propose FedCDI, a framework for causal discovery in federated settings where data cannot leave local sites. Rather than sharing raw samples, clients exchange belief updates over causal graphs, enabling privacy-preserving inference from both shared and heterogeneous interventional data.