Uncertainty in Artificial Intelligence
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Pearl's Calculus of Intervention Is Complete
Yimin Huang, Marco Valtorta
Abstract:
This paper is concerned with graphical criteria that can be used to solve the problem of identifying casual effects from nonexperimental data in a causal Bayesian network structure, i.e., a directed acyclic graph that represents causal relationships. We first review Pearl's work on this topic [Pearl, 1995], in which several useful graphical criteria are presented. Then we present a complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem. By exploiting the completeness of this algorithm, we prove that the three basic do-calculus rules that Pearl presents are complete, in the sense that, if a causal effect is identifiable, there exists a sequence of applications of the rules of the do-calculus that transforms the causal effect formula into a formula that only includes observational quantities.
Keywords:
Pages: 217-224
PS Link:
PDF Link: /papers/06/p217-huang.pdf
BibTex:
@INPROCEEDINGS{Huang06,
AUTHOR = "Yimin Huang and Marco Valtorta",
TITLE = "Pearl's Calculus of Intervention Is Complete",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "217--224"
}


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