Local Characterizations of Causal Bayesian Networks

  • Elias Bareinboim
  • Carlos Brito
  • Judea Pearl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7205)

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.

Keywords

Directed Acyclic Graph Probabilistic Interpretation Manipulate Variable Interventional Distribution Causal Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Elias Bareinboim
    • 1
  • Carlos Brito
    • 2
  • Judea Pearl
    • 1
  1. 1.Cognitive Systems Laboratory, Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA
  2. 2.Computer Science DepartmentFederal University of CearáBrazil

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