Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
On the Logic of Causal Models
Dan Geiger, Judea Pearl
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.
Pages: 136-147
PS Link:
PDF Link: /papers/88/p136-geiger.pdf
AUTHOR = "Dan Geiger and Judea Pearl",
TITLE = "On the Logic of Causal Models",
BOOKTITLE = "Proceedings of the Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1988",
PAGES = "136--147"

hosted by DSL   •   site info   •   help