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Abstract Details
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Activity Number:
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214
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Type:
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Invited
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #300164 |
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Title:
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Causality, Conditional Independence, and Graphical Separation in Settable Systems
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Author(s):
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Karim Chalak*+ and Halbert White
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Companies:
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Boston College and University of California at San Diego
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Address:
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Dept. of Economics, Boston College, Chestnut Hill, MA, 02467 , USA
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Keywords:
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causality ;
conditional independence ;
d-separation ;
Reichenbach principle ;
settable systems
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Abstract:
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We study the connections between causal relations and conditional independence within the settable systems extension of the Pearl Causal Model. Our analysis clearly distinguishes between causal notions and probabilistic notions and does not formally rely on graphical representations. We provide definitions in terms of functional dependence for direct, indirect, and total causality as well as for indirect causality via and exclusive of a set of variables. We apply these notions to formally connect causal and probabilistic conditions for conditional dependence among random vectors in structural systems. We state and prove the conditional Reichenbach principle of common cause, obtaining the classical Reichenbach principle as a corollary. Finally, we apply our approach to study notions of graphical separation, such as d-separation and D-separation in the artificial intelligence literature.
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