A new characterization of the experimental implications of causal Bayesian networks

Authors:
Jin Tian;Judea Pearl
Affiliations:
Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA;Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA
Venue:
Eighteenth national conference on Artificial intelligence
Year:
2002

Citing 4
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Abstract

We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network. We show that such distributions must adhere to three norms of coherence, and we demonstrate the use of these norms as inferential tools in tasks of learning and identification. Testable coherence norms are subsequently derived for networks containing unmeasured variables.