A Survey on Federated Causal Discovery and Inference

The paper presents a comprehensive survey on Federated Causal Discovery (FCD) and Federated Causal Inference (FCI), addressing the challenges of conducting causal analysis with distributed data while adhering to privacy regulations. It organizes FCD methods based on how structures are learned, data partitioned, and the structural knowledge obtained, and categorizes FCI methods by target estimand and estimation strategy, including classical and deep generative approaches. This work is significant as it formalizes the relationship between FCD and FCI, proposing a unified pipeline that enhances causal reasoning in federated settings while identifying key areas for future research, such as privacy and communication efficiency.