A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
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Abstract (arXiv)
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm can currently handle cycles, latent variables and selection bias (CLS) simultaneously. I therefore introduce an algorithm called Cyclic Causal Inference (CCI) that makes sound inferences with a conditional independence oracle under CLS, provided that we can represent the cyclic causal process as a non-recursive linear structural equation model with independent errors. Empirical results show that CCI outperforms CCD in the cyclic case as well as rivals FCI and RFCI in the acyclic case.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2018.
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Citing papers per year: 2021: 3; 2022: 6; 2023: 2; 2024: 2; 2025: 2.
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- 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
- 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
- 2023Discovering Causal Relations and Equations from Datacited by 33
- 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
- 2022GFlowCausal: Generative Flow Networks for Causal Discoverycited by 11
- 2022Learning Relational Causal Models with Cycles through Relational Acyclificationcited by 6
- 2022Sample-Specific Root Causal Inference with Latent Variablescited by 6
- 2024Are Bayesian networks typically faithful?cited by 6
- 2023Establishing Markov Equivalence in Cyclic Directed Graphscited by 5
- 2021Causality and independence in perfectly adapted dynamical systemscited by 4
- 2022Relational Causal Models with Cycles:Representation and Reasoningcited by 4
- 2022Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variablescited by 2
3 more
- 2025Relational Causal Discovery with Latent Confounderscited by 2
- 2024Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounderscited by 1
- 2025σ-Maximal Ancestral Graphscited by 0
- 2025Relational Causal Discovery with Latent Confounders2 Jul 2025
- 2025σ-Maximal Ancestral Graphs30 Jun 2025
- 2024Are Bayesian networks typically faithful?21 Oct 2024
- 2024Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders23 Jan 2024
- 2023Establishing Markov Equivalence in Cyclic Directed Graphs1 Sep 2023
- 2023Discovering Causal Relations and Equations from Data21 May 2023
- 2022Sample-Specific Root Causal Inference with Latent Variables27 Oct 2022
- 2022GFlowCausal: Generative Flow Networks for Causal Discovery15 Oct 2022
- 2022Learning Relational Causal Models with Cycles through Relational Acyclification25 Aug 2022
- 2022Causal Structure Learning: a Combinatorial Perspective2 Jun 2022
- 2022Relational Causal Models with Cycles:Representation and Reasoning22 Feb 2022
- 2022Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables20 Feb 2022
- 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
- 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
- 2023Discovering Causal Relations and Equations from Datacited by 33
- 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
- 2022GFlowCausal: Generative Flow Networks for Causal Discoverycited by 11
- 2022Learning Relational Causal Models with Cycles through Relational Acyclificationcited by 6
- 2022Sample-Specific Root Causal Inference with Latent Variablescited by 6
- 2024Are Bayesian networks typically faithful?cited by 6
- 2023Establishing Markov Equivalence in Cyclic Directed Graphscited by 5
- 2021Causality and independence in perfectly adapted dynamical systemscited by 4
- 2022Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variablescited by 2
- 2025Relational Causal Discovery with Latent Confounderscited by 2
12 most cited of 14 All 14 (JSON)
- 2025Relational Causal Discovery with Latent Confounders2 Jul 2025
- 2025σ-Maximal Ancestral Graphs30 Jun 2025
- 2024Are Bayesian networks typically faithful?21 Oct 2024
- 2024Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders23 Jan 2024
- 2023Establishing Markov Equivalence in Cyclic Directed Graphs1 Sep 2023
- 2023Discovering Causal Relations and Equations from Data21 May 2023
- 2022Sample-Specific Root Causal Inference with Latent Variables27 Oct 2022
- 2022GFlowCausal: Generative Flow Networks for Causal Discovery15 Oct 2022
- 2022Learning Relational Causal Models with Cycles through Relational Acyclification25 Aug 2022
- 2022Causal Structure Learning: a Combinatorial Perspective2 Jun 2022
- 2022Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables20 Feb 2022
- 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias22 Oct 2021
12 newest of 14 All 14 (JSON)