mlsanityData to 14 Oct 2026

Strong Completeness and Faithfulness in Bayesian Networks

arXiv 1302.4973 ↗First posted 20 Feb 2013cs.AIcited by 45

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Abstract (arXiv)

A completeness result for d-separation applied to discrete Bayesian networks is presented and it is shown that in a strong measure-theoretic sense almost all discrete distributions for a given network structure are faithful; i.e. the independence facts true of the distribution are all and only those entailed by the network structure.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Feb 2013.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 14; 2024: 5; 2025: 4; 2026: 9.

45 papers.

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  1. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  2. 2024Robust agents learn causal world modelscited by 81
  3. 2023Explaining Predictive Uncertainty with Information Theoretic Shapley Valuescited by 36
  4. 2022Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomenacited by 21
  5. 2023Causal Deep Learningcited by 20
  6. 2021Invariant Policy Learning: A Causal Perspectivecited by 17
  7. 2022Selection, Ignorability and Challenges With Causal Fairnesscited by 16
  8. 2022Distributed Learning of Generalized Linear Causal Networkscited by 13
  9. 2022Detecting hidden confounding in observational data using multiple environmentscited by 13
  10. 2023Subset verification and search algorithms for causal DAGscited by 12
  11. 2021ML4C: Seeing Causality Through Latent Vicinitycited by 11
  12. 2023Toward Falsifying Causal Graphs Using a Permutation-Based Testcited by 11
33 more
  1. 2021A Topological Perspective on Causal Inferencecited by 10
  2. 2023Active causal structure learning with advicecited by 10
  3. 2021Obtaining Causal Information by Merging Datasets with MAXENTcited by 9
  4. 2023Expert-Aided Causal Discovery of Ancestral Graphscited by 8
  5. 2023Towards Bounding Causal Effects under Markov Equivalencecited by 8
  6. 2024Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learningcited by 7
  7. 2021Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignmentcited by 6
  8. 2024Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Methodcited by 6
  9. 2024Are Bayesian networks typically faithful?cited by 6
  10. 2021Causality and independence in perfectly adapted dynamical systemscited by 4
  11. 2023Reinterpreting causal discovery as the task of predicting unobserved joint statisticscited by 4
  12. 2023Neuro-Causal Factor Analysiscited by 4
  13. 2023Results on Counterfactual Invariancecited by 4
  14. 2025Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learningcited by 3
  15. 2023Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approachcited by 2
  16. 2023On the Independencies Hidden in the Structure of a Probabilistic Logic Programcited by 2
  17. 2023Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)cited by 2
  18. 2025Characterization and Learning of Causal Graphs from Hard Interventionscited by 2
  19. 2025Identifiability by common backdoor in summary causal graphs of time seriescited by 2
  20. 2022Navigating causal deep learningcited by 1
  21. 2026Transformer Is Inherently a Causal Learnercited by 1
  22. 2026Robust Weighted Triangulation of Causal Effects Under Model Uncertaintycited by 1
  23. 2022Differentiable and Transportable Structure Learningcited by 0
  24. 2023Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledgecited by 0
  25. 2024Learning linear acyclic causal model including Gaussian noise using ancestral relationshipscited by 0
  26. 2025Sequential Causal Discovery with Noisy Language Model Priorscited by 0
  27. 2026Masking Causality and Conditional Dependencecited by 0
  28. 2026Characterizing and Identifying Separable Graphical Modelscited by 0
  29. 2026Relaxing Faithfulness with Intervention-Only Causal Discoverycited by 0
  30. 2026A Causal Markov Condition for Valuecited by 0
  31. 2026High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumptioncited by 0
  32. 2026From probability to causality in probabilistic logic programmingcited by 0
  33. 2026AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settingscited by 0