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Causal Inference and Causal Explanation with Background Knowledge

arXiv 1302.4972 ↗First posted 20 Feb 2013cs.AIcited by 168

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Abstract (arXiv)

This paper presents correct algorithms for answering the following two questions; (i) Does there exist a causal explanation consistent with a set of background knowledge which explains all of the observed independence facts in a sample? (ii) Given that there is such a causal explanation what are the causal relationships common to every such causal explanation?

Key facts. Cited by 168 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2013.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 22; 2022: 16; 2023: 32; 2024: 28; 2025: 31; 2026: 39.

168 papers.

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  1. 2021A survey of Bayesian Network structure learningcited by 85
  2. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  3. 2023Causal Discovery with Language Models as Imperfect Expertscited by 62
  4. 2021gCastle: A Python Toolbox for Causal Discoverycited by 60
  5. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  6. 2023Causal Order: The Key to Leveraging Imperfect Experts in Causal Inferencecited by 46
  7. 2022From Statistical to Causal Learningcited by 40
  8. 2023Discovering Causal Relations and Equations from Datacited by 33
  9. 2023A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variablescited by 33
  10. 2024Large Language Models are Effective Priors for Causal Graph Discoverycited by 23
  11. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  12. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19
38 more
  1. 2022Verification and search algorithms for causal DAGscited by 18
  2. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  3. 2024Standardizing Structural Causal Modelscited by 17
  4. 2021Matching a Desired Causal State via Shift Interventionscited by 16
  5. 2024Adjustment Identification Distance: A gadjid for Causal Structure Learningcited by 16
  6. 2024Large Language Models for Constrained-Based Causal Discoverycited by 15
  7. 2024Causal Discovery with Fewer Conditional Independence Testscited by 13
  8. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  9. 2023Subset verification and search algorithms for causal DAGscited by 12
  10. 2024Recursive Causal Discoverycited by 12
  11. 2021ML4C: Seeing Causality Through Latent Vicinitycited by 11
  12. 2022Towards Privacy-Aware Causal Structure Learning in Federated Settingcited by 11
  13. 2021Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagationcited by 10
  14. 2021Typing assumptions improve identification in causal discoverycited by 10
  15. 2022Causal discovery for observational sciences using supervised machine learningcited by 10
  16. 2023Local Causal Discovery for Estimating Causal Effectscited by 10
  17. 2023Active causal structure learning with advicecited by 10
  18. 2021On the Sample Complexity of Causal Discovery and the Value of Domain Expertisecited by 9
  19. 2022Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applicationscited by 9
  20. 2023Do we become wiser with time? On causal equivalence with tiered background knowledgecited by 9
  21. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  22. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  23. 2022A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causescited by 8
  24. 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
  25. 2023Bootstrap aggregation and confidence measures to improve time series causal discoverycited by 8
  26. 2024DCILP: A Distributed Approach for Large-Scale Causal Structure Learningcited by 8
  27. 2025Extremely Greedy Equivalence Searchcited by 8
  28. 2021Towards Efficient Local Causal Structure Learningcited by 7
  29. 2023CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Datacited by 7
  30. 2023Adaptivity Complexity for Causal Graph Discoverycited by 7
  31. 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Frameworkcited by 7
  32. 2025LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discoverycited by 7
  33. 2021A Local Method for Identifying Causal Relations under Markov Equivalencecited by 6
  34. 2021Interactive Causal Structure Discovery in Earth System Sciencescited by 6
  35. 2022Quantitative probing: Validating causal models using quantitative domain knowledgecited by 6
  36. 2023Efficient Enumeration of Markov Equivalent DAGscited by 6
  37. 2023A Survey of Methods, Challenges and Perspectives in Causalitycited by 6
  38. 2024Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approachcited by 6

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