A New Characterization of the Experimental Implications of Causal Bayesian Networks

Jin Tian and Judea Pearl, University of California, Los Angeles

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.


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