A factorization criterion for acyclic directed mixed graphs
Not on the map. Why?
Abstract (arXiv)
Acyclic directed mixed graphs, also known as semi-Markov models represent the conditional independence structure induced on an observed margin by a DAG model with latent variables. In this paper we present a factorization criterion for these models that is equivalent to the global Markov property given by (the natural extension of) d-separation.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Jun 2014.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 1; 2025: 4.
8 papers.
- 2021A survey of Bayesian Network structure learningcited by 87
- 2021Integer Programming for Causal Structure Learning in the Presence of Latent Variablescited by 15
- 2021Causal Homotopycited by 4
- 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounderscited by 4
- 2024Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independenciescited by 1
- 2025ExMAG: Learning of Maximally Ancestral Graphscited by 1
- 2025Graphical Models for Decision-Making: Integrating Causality and Game Theorycited by 0
- 2025Graph Distance Based on Cause-Effect Estimands with Latentscited by 0
- 2025Graph Distance Based on Cause-Effect Estimands with Latents28 Oct 2025
- 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounders11 Aug 2025
- 2025Graphical Models for Decision-Making: Integrating Causality and Game Theory16 Apr 2025
- 2025ExMAG: Learning of Maximally Ancestral Graphs11 Mar 2025
- 2024Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies22 Sep 2024
- 2021A survey of Bayesian Network structure learning23 Sep 2021
- 2021Causal Homotopy20 Sep 2021
- 2021Integer Programming for Causal Structure Learning in the Presence of Latent Variables5 Feb 2021