Federated Causal Discovery with Unknown Interventions
Published:Dec 29, 2025 17:30
•1 min read
•ArXiv
Analysis
This paper addresses a critical challenge in federated causal discovery: handling heterogeneous and unknown interventions across clients. The proposed I-PERI algorithm offers a solution by recovering a tighter equivalence class (Φ-CPDAG) and providing theoretical guarantees on convergence and privacy. This is significant because it moves beyond idealized assumptions of shared causal models, making federated causal discovery more practical for real-world scenarios like healthcare where client-specific interventions are common.
Key Takeaways
Reference
“The paper proposes I-PERI, a novel federated algorithm that first recovers the CPDAG of the union of client graphs and then orients additional edges by exploiting structural differences induced by interventions across clients.”