Uncertainty in Artificial Intelligence
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On the Logic of Causal Models
Dan Geiger, Judea Pearl
Abstract:
This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of conditional independence relationships. We show that DAGs offer polynomially sound and complete inference mechanisms for inferring conditional independence relationships from a given causal set of such relationships. As a consequence, d-separation, a graphical criterion for identifying independencies in a DAG, is shown to uncover more valid independencies then any other criterion. In addition, we employ the Armstrong property of conditional independence to show that the dependence relationships displayed by a DAG are inherently consistent, i.e. for every DAG D there exists some probability distribution P that embodies all the conditional independencies displayed in D and none other.
Keywords: null
Pages: 3-14
PS Link:
PDF Link: /papers/88/p3-geiger.pdf
BibTex:
@INPROCEEDINGS{Geiger88,
AUTHOR = "Dan Geiger and Judea Pearl",
TITLE = "On the Logic of Causal Models",
BOOKTITLE = "Uncertainty in Artificial Intelligence 4 Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1988",
PAGES = "3--14"
}


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