Uncertainty in Artificial Intelligence
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Causal Discovery from Changes
Jin Tian, Judea Pearl
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 are 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.
Keywords: causal discovery, learning Bayesian networks
Pages: 512-521
PS Link:
PDF Link: /papers/01/p512-tian.pdf
BibTex:
@INPROCEEDINGS{Tian01,
AUTHOR = "Jin Tian and Judea Pearl",
TITLE = "Causal Discovery from Changes",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "512--521"
}


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