Regret-Based Federated Causal Discovery with Unknown Interventions

📰 ArXiv cs.AI

Learn how to apply regret-based federated causal discovery to handle unknown interventions and decentralization in causal learning

advanced Published 1 Jun 2026

Action Steps

Apply federated causal discovery to decentralized data using a regret-based approach
Handle unknown interventions by incorporating intervention-aware loss functions
Configure the federated learning framework to account for client-specific policies and protocols
Test the robustness of the approach using simulated and real-world datasets
Compare the performance of regret-based federated causal discovery with traditional causal discovery methods

Who Needs to Know This

Data scientists and researchers working on causal discovery and federated learning can benefit from this approach to handle complex, decentralized data

Key Insight

💡 Regret-based federated causal discovery can effectively handle unknown interventions and decentralization in causal learning

Key Takeaways

Learn how to apply regret-based federated causal discovery to handle unknown interventions and decentralization in causal learning

Full Article

Title: Regret-Based Federated Causal Discovery with Unknown Interventions

Abstract:
arXiv:2512.23626v2 Announce Type: replace Abstract: Most causal discovery methods recover a completed partially directed acyclic graph representing a Markov equivalence class from observational data. Recent work has extended these methods to federated settings to address data decentralization and privacy constraints, but often under idealized assumptions that all clients share the same causal model. Such assumptions are unrealistic in practice, as client-specific policies or protocols, for examp

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