Directed constraint networks: a relational framework for causal modeling

Authors:
Rina Dechter;Judea Pearl
Affiliations:
Information & Computer Science, University of California, Irvine, Irvine, CA;Computer Science Department, University of California, Los Angeles, Los Angeles, CA
Venue:
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Year:
1991

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Abstract

Normally, constraint networks are undirected, since constraints merely tell us which sets of values are compatible, and compatibility is a symmetrical relationship. In contrast, causal models use directed links, conveying cause-effect asymmetries. In this paper we give a relational semantics to this directionality, thus explaining why prediction is easy while diagnosis and planning are hard. We use this semantics to show that certain relations possess intrinsic directionalities, similar to those characterizing causal influences. We also use this semantics to decide when and how an unstructured set of symmetrical constraints can be configured so as to form a directed causal theory.