mlsanityData to 7 Oct 2026

Federated Causality Learning with Explainable Adaptive Optimization

arXiv 2312.05540 ↗First posted 9 Dec 2023cs.LGcited by 11

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Abstract (arXiv)

Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it is very hard to learn causal graphs from dispersed data, since these data may have different distributions. In this paper, we propose a federated causal discovery strategy (FedCausal) to learn the unified global causal graph from decentralized heterogeneous data. We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data. Unlike other federated causal learning algorithms, FedCausal unifies the local and global optimizations into a complete directed acyclic graph (DAG) learning process with a flexible optimization objective. We prove that this optimization objective has a high interpretability and can adaptively handle homogeneous and heterogeneous data. Experimental results on synthetic and real datasets show that FedCausal can effectively deal with non-independently and identically distributed (non-iid) data and has a superior performance.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 9 Dec 2023.

Builds on

13 of 30 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  4. 2019Gradient-Based Neural DAG Learningcited by 227
  5. 2019Learning Sparse Nonparametric DAGscited by 207
  6. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
  7. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  8. 2019Secure Federated Matrix Factorizationcited by 102
  9. 2019Masked Gradient-Based Causal Structure Learningcited by 89
  10. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  11. 2021Towards Federated Bayesian Network Structure Learning with Continuous Optimizationcited by 31
  12. 2021Federated Causal Inference in Heterogeneous Observational Datacited by 24
1 more
  1. 2022Distributed Learning of Generalized Linear Causal Networkscited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 5.

11 papers.

  1. 2025Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Recordscited by 2
  2. 2025Regret-Based Federated Causal Discovery with Unknown Interventionscited by 2
  3. 2025Federated Causal Inference in Healthcare: Methods, Challenges, and Applicationscited by 1
  4. 2025Interpretable Clustering with Adaptive Heterogeneous Causal Structure Learning in Mixed Observational Datacited by 1
  5. 2026Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoningcited by 1
  6. 2025Drift-Aware Federated Learning: A Causal Perspectivecited by 0
  7. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  8. 2026Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretabilitycited by 0
  9. 2026Distributed Dynamic Invariant Causal Prediction in Environmental Time Seriescited by 0
  10. 2026A Survey on Federated Causal Discovery and Inferencecited by 0
  11. 2026SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoningcited by 0