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Towards Complete Causal Explanation with Expert Knowledge

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arxiv 2407.07338 v4 pith:HZLSBTBG submitted 2024-07-10 stat.ML cs.DMcs.LGstat.ME

classification stat.MLcs.DMcs.LGstat.ME
keywords orientationgraphknowledgeancestralclassequivalenceessentialmarkov
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Identifying Conditional Causal Effects in MPDAGs

    cs.AI 2025-07 accept novelty 6.0 of 10

    The paper gives sound and complete methods for identifying conditional causal effects when the causal graph is known only up to an MPDAG.

  2. Integrating Background Knowledge for Scalable Causal Discovery

    stat.ML 2026-07 accept novelty 5.0 of 10

    Integrating adjacency, orientation, and gap background knowledge during PC, SNAP, MB-by-MB, LDECC, and LOAD reduces CI tests and improves causal-effect estimates under standard assumptions.

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