mlsanityData to 14 Oct 2026

A Survey on Federated Causal Discovery and Inference

arXiv 2606.23741 ↗First posted 21 Jun 2026cs.LG cs.AI cs.DCcited by 0

On the map: LLM reasoning → Causal reasoning in LLMs

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. Builds on 58 of its 125 references that are in the map. First posted on arXiv 21 Jun 2026.

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  1. 2023CLadder: Assessing Causal Reasoning in Language Models7 Dec
  2. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causality28 Apr

Other references · 56

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  28. 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysiscited by 4
  29. 2025FedGES: A Federated Learning Approach for BN Structure Learningcited by 4
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  31. 2024Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysiscited by 3
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  33. 2025Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulantscited by 3
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