A Survey on Federated Causal Discovery and Inference

2026-06-21 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new survey published on 2026-06-21 systematically reviews Federated Causal Discovery (FCD) and Inference (FCI), addressing a gap in comprehensive resources for this interdisciplinary field. It organizes FCD solutions along three axes: methodological paradigm, federation topology, and structural scope, based on how causal structures are learned, data partitioned, and structural knowledge shared. The survey also examines practical FCD dimensions like temporal dynamics, data heterogeneity, missing data, and non-identical variable sets. For FCI, methods are categorized by target estimand (average versus individualized/conditional treatment effects) and estimation strategy, from classical weighting to modern deep generative architectures. Crucially, it formalizes FCD and FCI as complementary stages within a unified federated causal reasoning pipeline, where FCD provides structural knowledge for FCI's effect estimation. The work concludes by highlighting shared concerns such as privacy, communication efficiency, theoretical guarantees, and application domains, alongside identifying open challenges.

Key takeaway

For research scientists and data scientists working with distributed, privacy-sensitive datasets, this survey provides a critical framework for understanding federated causal reasoning. You should utilize its multi-dimensional taxonomies to navigate FCD and FCI methodologies, ensuring your causal analyses comply with privacy regulations while maintaining robust effect estimation. Consider the identified open challenges to guide your future research directions in this rapidly evolving field.

Key insights

Federated Causal Discovery and Inference are complementary stages in a unified pipeline for privacy-preserving causal reasoning.

Principles

• FCD design involves structure learning, data partitioning, and knowledge scope.
• FCD supplies structural knowledge for valid FCI effect estimation.

Method

The survey proposes a systematic review of FCD and FCI through multi-dimensional taxonomies, organizing FCD by methodological paradigm, federation topology, and structural scope, and FCI by target estimand and estimation strategy.

In practice

• Apply FCD/FCI in scenarios with distributed, privacy-sensitive data.
• Consider temporal dynamics and data heterogeneity in FCD solutions.

Topics

• Federated Learning
• Causal Discovery
• Causal Inference
• Privacy Regulations
• Data Heterogeneity
• Distributed Systems

Best for: AI Scientist, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.