A Survey on Federated Causal Discovery and Inference

2026-06-21

AI Summary

This study addresses the challenges of reliable causal discovery and inference in distributed settings where data cannot be centralized due to privacy or communication constraints. It presents the first unified framework that integrates federated causal discovery (FCD) and federated causal inference (FCI) into a cohesive paradigm for federated causal reasoning. The work introduces a three-dimensional taxonomy for FCD based on structure learning, data partitioning, and knowledge acquisition, and further develops a multidimensional classification encompassing methodological paradigms, federated topologies, structural scopes, types of treatment effects, and estimation strategies—including weighting methods and deep generative models—to clarify the theoretical connections between FCD and FCI. By systematically mapping the research landscape, the paper identifies shared challenges such as privacy preservation, communication efficiency, and theoretical guarantees, offering a comprehensive foundation for future advancements.

Abstract

Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making. In practice, data for reliable causal analysis are often distributed across institutions and cannot be centralized due to privacy regulations or communication constraints. Federated learning (FL) addresses this by enabling collaborative analysis without raw data sharing, giving rise to the rapidly growing field of federated causal discovery (FCD) and inference (FCI). However, the interdisciplinary nature of this field and the absence of a comprehensive survey present barriers to entry for researchers. This paper bridges that gap by providing a systematic review through multi-dimensional taxonomies. Grounded in the three core design decisions underlying any FCD solution, namely how structures are learned, how data are partitioned, and what structural knowledge each party obtains, we organize FCD along three axes: methodological paradigm, federation topology, and structural scope. We further examine key practical dimensions, including temporal dynamics, data heterogeneity, missing data, and non-identical variable sets. For FCI, we categorize methods by target estimand (average versus individualized/conditional treatment effects) and by estimation strategy, from classical weighting methods to modern deep generative architectures. Unlike prior works that treat FCD and FCI separately, we formalize their connection as complementary stages of a unified federated causal reasoning pipeline, where FCD supplies the structural knowledge required for valid effect estimation in FCI. Finally, we highlight their shared concerns regarding privacy, communication efficiency, theoretical guarantees, and application domains, and conclude by identifying open challenges for future research.

Problem

• federated causal discovery
• federated causal inference
• causal reasoning
• data decentralization
• privacy constraints

Innovation

• Federated Causal Discovery
• Federated Causal Inference
• Causal Reasoning
• Privacy-Preserving Learning
• Federated Learning