A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models
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Abstract (arXiv)
Although the concept of d-separation was originally defined for directed acyclic graphs (see Pearl 1988), there is a natural extension of he concept to directed cyclic graphs. When exactly the same set of d-separation relations hold in two directed graphs, no matter whether respectively cyclic or acyclic, we say that they are Markov equivalent. In other words, when two directed cyclic graphs are Markov equivalent, the set of distributions that satisfy a natural extension of the Global Directed Markov condition (Lauritzen et al. 1990) is exactly the same for each graph. There is an obvious exponential (in the number of vertices) time algorithm for deciding Markov equivalence of two directed cyclic graphs; simply chech all of the d-separation relations in each graph. In this paper I state a theorem that gives necessary and sufficient conditions for the Markov equivalence of two directed cyclic graphs, where each of the conditions can be checked in polynomial time. Hence, the theorem can be easily adapted into a polynomial time algorithm for deciding the Markov equivalence of two directed cyclic graphs. Although space prohibits inclusion of correctness proofs, they are fully described in Richardson (1994b).
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2013.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 2.
7 papers.
- 2023Causal Modeling with Stationary Diffusionscited by 19
- 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
- 2023Establishing Markov Equivalence in Cyclic Directed Graphscited by 5
- 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelscited by 4
- 2022Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decompositioncited by 1
- 2023Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networkscited by 1
- 2025Lower Bounds on the Size of Markov Equivalence Classescited by 1
- 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models25 Sep 2025
- 2025Lower Bounds on the Size of Markov Equivalence Classes26 Jun 2025
- 2023Causal Modeling with Stationary Diffusions26 Oct 2023
- 2023Establishing Markov Equivalence in Cyclic Directed Graphs1 Sep 2023
- 2023Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks6 Jan 2023
- 2022Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition25 Nov 2022
- 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models20 May 2022