Towards Complete Causal Explanation with Expert Knowledge
Not on the map. Why?
Abstract (arXiv)
We study the problem of restricting a Markov equivalence class of maximal ancestral graphs (MAGs) to only those MAGs that contain certain edge marks, which we refer to as expert or orientation knowledge. Such a restriction of the Markov equivalence class can be uniquely represented by a restricted essential ancestral graph. Our contributions are several-fold. First, we prove certain properties for the entire Markov equivalence class including a conjecture from Ali et al. (2009). Second, we present several new sound graphical orientation rules for adding orientation knowledge to an essential ancestral graph. We also show that some orientation rules of Zhang (2008b) are not needed for restricting the Markov equivalence class with orientation knowledge. Third, we provide an algorithm for including this orientation knowledge and show that in certain settings the output of our algorithm is a restricted essential ancestral graph. Finally, outside of the specified settings, we provide an algorithm for checking whether a graph is a restricted essential graph and discuss its runtime. This work can be seen as a generalization of Meek (1995) to settings which allow for latent confounding.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2024.
Builds on
0 of 0 references.
No references here yet.
Built on by
Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
- 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
- 2025Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasetscited by 2
- 2024New Rules for Causal Identification with Background Knowledgecited by 1
- 2024Retrieving Classes of Causal Orders with Inconsistent Knowledge Basescited by 0
- 2026Integrating Background Knowledge for Scalable Causal Discoverycited by 0
- 2026Integrating Background Knowledge for Scalable Causal Discovery11 Jul 2026
- 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning5 Mar 2026
- 2025Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets27 Mar 2025
- 2025When Selection Meets Intervention: Additional Complexities in Causal Discovery10 Mar 2025
- 2024Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases18 Dec 2024
- 2024New Rules for Causal Identification with Background Knowledge21 Jul 2024