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  1. A note on the correctness of the causal ordering algorithm.Denver Dash & Marek J. Druzdzel - 2008 - Artificial Intelligence 172 (15):1800-1808.
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  • Belief networks revisited.Judea Pearl - 1993 - Artificial Intelligence 59 (1-2):49-56.
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  • Learning Bayesian networks from data: An information-theory based approach.Jie Cheng, Russell Greiner, Jonathan Kelly, David Bell & Weiru Liu - 2002 - Artificial Intelligence 137 (1-2):43-90.
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  • (1 other version)Jon Williamson bayesian nets and causality.Clark Glymour - 2009 - British Journal for the Philosophy of Science 60 (4):849-855.
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  • Detection of unfaithfulness and robust causal inference.Jiji Zhang & Peter Spirtes - 2008 - Minds and Machines 18 (2):239-271.
    Much of the recent work on the epistemology of causation has centered on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition. Philosophical discussions of the latter condition have exhibited situations in which it is likely to fail. This paper studies the Causal Faithfulness Condition as a conjunction of weaker conditions. We show that some of the weaker conjuncts can be empirically tested, and hence do not have to be assumed a priori. Our results lead to (...)
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  • A local method for identifying causal relations under Markov equivalence.Zhuangyan Fang, Yue Liu, Zhi Geng, Shengyu Zhu & Yangbo He - 2022 - Artificial Intelligence 305 (C):103669.
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  • Response to Glymour. [REVIEW]Jon Williamson - 2009 - British Journal for the Philosophy of Science 60 (4):857-860.
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  • Decomposition of structural learning about directed acyclic graphs.Xianchao Xie, Zhi Geng & Qiang Zhao - 2006 - Artificial Intelligence 170 (4-5):422-439.
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