On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias

DOI10.1016/j.artint.2008.08.001zbMath1184.68434OpenAlexW2134652049MaRDI QIDQ2389689

Jiji Zhang

Publication date: 17 July 2009

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2008.08.001

zbMATH Keywords

Bayesian networkslatent variablescausal modelsMarkov equivalenceancestral graphsautomated causal discovery

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

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Uses Software

• TETRAD

Cites Work

• Markov equivalence for ancestral graphs
• Causation, prediction, and search
• A characterization of Markov equivalence classes for acyclic digraphs
• Stratified exponential families: Graphical models and model selection
• Ancestral graph Markov models.
• On the Markov equivalence of maximal ancestral graphs
• Independence, Invariance and the Causal Markov Condition
• 10.1162/153244303321897717
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

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