Uncertainty in Artificial Intelligence
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A theoretical study of Y structures for causal discovery
Subramani Mani, Peter Spirtes, Gregory Cooper
Abstract:
There are several existing algorithms that under appropriate assumptions can reliably identify a subset of the underlying causal relationships from observational data. This paper introduces the first computationally feasible score-based algorithm that can reliably identify causal relationships in the large sample limit for discrete models, while allowing for the possibility that there are unobserved common causes. In doing so, the algorithm does not ever need to assign scores to causal structures with unobserved common causes. The algorithm is based on the identification of so called Y substructures within Bayesian network structures that can be learned from observational data. An example of a Y substructure is A -> C, B -> C, C -> D. After providing background on causal discovery, the paper proves the conditions under which the algorithm is reliable in the large sample limit.
Keywords:
Pages: 314-323
PS Link:
PDF Link: /papers/06/p314-mani.pdf
BibTex:
@INPROCEEDINGS{Mani06,
AUTHOR = "Subramani Mani and Peter Spirtes and Gregory Cooper",
TITLE = "A theoretical study of Y structures for causal discovery",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "314--323"
}


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