mlsanityData to 7 Oct 2026

A Discovery Algorithm for Directed Cyclic Graphs

arXiv 1302.3599 ↗First posted 13 Feb 2013cs.AIcited by 33

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Abstract (arXiv)

Directed acyclic graphs have been used fruitfully to represent causal strucures (Pearl 1988). However, in the social sciences and elsewhere models are often used which correspond both causally and statistically to directed graphs with directed cycles (Spirtes 1995). Pearl (1993) discussed predicting the effects of intervention in models of this kind, so-called linear non-recursive structural equation models. This raises the question of whether it is possible to make inferences about causal structure with cycles, form sample data. In particular do there exist general, informative, feasible and reliable precedures for inferring causal structure from conditional independence relations among variables in a sample generated by an unknown causal structure? In this paper I present a discovery algorithm that is correct in the large sample limit, given commonly (but often implicitly) made plausible assumptions, and which provides information about the existence or non-existence of causal pathways from one variable to another. The algorithm is polynomial on sparse graphs.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2013.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 7; 2024: 6; 2025: 9; 2026: 5.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  3. 2023Discovering Causal Relations and Equations from Datacited by 33
  4. 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
  5. 2024Recursive Causal Discoverycited by 12
  6. 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
  7. 2022Learning Relational Causal Models with Cycles through Relational Acyclificationcited by 6
  8. 2024Are Bayesian networks typically faithful?cited by 6
  9. 2023Establishing Markov Equivalence in Cyclic Directed Graphscited by 5
  10. 2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacencycited by 5
  11. 2022Relational Causal Models with Cycles:Representation and Reasoningcited by 4
  12. 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Datacited by 4
21 more
  1. 2025Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Datacited by 4
  2. 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounderscited by 4
  3. 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelscited by 4
  4. 2025Root Cause Analysis of Outliers in Unknown Cyclic Graphscited by 4
  5. 2022Fast Parallel Bayesian Network Structure Learningcited by 3
  6. 2023Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studiescited by 3
  7. 2025Causal Discovery for Linear Non-Gaussian Models with Disjoint Cyclescited by 3
  8. 2025CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulationscited by 3
  9. 2025Causal Structure and Representation Learning with Biomedical Applicationscited by 3
  10. 2024Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounderscited by 1
  11. 2024Learning Cyclic Causal Models from Incomplete Datacited by 1
  12. 2024MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Datacited by 1
  13. 2025Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discoverycited by 1
  14. 2026On the Number of Conditional Independence Tests in Constraint-based Causal Discoverycited by 1
  15. 2021Identifiability of AMP chain graph modelscited by 0
  16. 2023Causal Discovery for fMRI data: Challenges, Solutions, and a Case Studycited by 0
  17. 2025Dynamic causal discovery in Alzheimer's disease through latent pseudotime modellingcited by 0
  18. 2026RECLAIM: Cyclic Causal Discovery Amid Measurement Noisecited by 0
  19. 2026SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targetscited by 0
  20. 2026Understanding Deterioration Random Effects for Causal Discovery in Infrastructure Managementcited by 0
  21. 2026Vector Space of Cyclescited by 0