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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7205))

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Abstract

The standard definition of causal Bayesian networks (CBNs) invokes a global condition according to which the distribution resulting from any intervention can be decomposed into a truncated product dictated by its respective mutilated subgraph. We analyze alternative formulations which emphasizes local aspects of the causal process and can serve therefore as more meaningful criteria for coherence testing and network construction. We first examine a definition based on “modularity” and prove its equivalence to the global definition. We then introduce two new definitions, the first interprets the missing edges in the graph, and the second interprets “zero direct effect” (i.e., ceteris paribus). We show that these formulations are equivalent but carry different semantic content.

This work was supported in parts by National Institutes of Health #1R01 LM009961-01, National Science Foundation #IIS-0914211 and #IIS-1018922, and Office of Naval Research #N000-14-09-1-0665 and #N00014-10-1-0933.

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Bareinboim, E., Brito, C., Pearl, J. (2012). Local Characterizations of Causal Bayesian Networks. In: Croitoru, M., Rudolph, S., Wilson, N., Howse, J., Corby, O. (eds) Graph Structures for Knowledge Representation and Reasoning. Lecture Notes in Computer Science(), vol 7205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29449-5_1

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  • DOI: https://doi.org/10.1007/978-3-642-29449-5_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29448-8

  • Online ISBN: 978-3-642-29449-5

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