Regret-Based Federated Causal Discovery with Unknown Interventions

ArXi:2512.23626v2 Announce Type: replace Most causal discovery methods recover a completed partially directed acyclic graph representing a Marko equivalence class from observational data. Recent work has extended these methods to federated settings to address data decentralization and privacy constraints, but often under idealized assumptions that all clients share the same causal model. Such assumptions are unrealistic in practice, as client-specific policies or protocols, for example, across hospitals, naturally induce heterogeneous and unknown interventions.