Federated Causal Discovery and Inference Surveyed

This paper provides a comprehensive survey of federated causal discovery and inference (FCD/FCI), a growing field that enables collaborative data analysis without centralizing sensitive information. It organizes existing methods based on how causal structures are learned, how data is partitioned, and the scope of knowledge obtained by each party. The survey also categorizes inference techniques by the type of causal effect being estimated and the strategy used for estimation. By framing FCD and FCI as complementary stages of a unified pipeline, the paper highlights their interconnectedness and identifies shared challenges such as privacy, communication efficiency, and theoretical guarantees, pointing towards future research directions. AI

IMPACT Provides a structured overview of federated causal reasoning methods, aiding researchers in understanding and advancing the field.

RANK_REASON The item is an academic survey paper on a specific subfield of AI research. [lever_c_demoted from research: ic=1 ai=1.0]

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• Federated Causal Discovery
• Federated learning

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