Causal discovery from changes

Authors:
Jin Tian;Judea Pearl
Affiliations:
Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA;Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA
Venue:
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Year:
2001

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Abstract

We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the underlying data-generating model. We analyze the classes of structures that axe equivalent relative to a stream of distributions produced by local changes, and devise algorithms that output graphical representations of these equivalence classes. We present experimental results, using simulated data, and examine the errors associated with detection of changes and recovery of structures.