Uncertainty in Artificial Intelligence
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Polynomial Constraints in Causal Bayesian Networks
Changsung Kang, Jin Tian
Abstract:
We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network with hidden variables. We show how we may reduce the complexity of the implicitization problem and make the problem tractable in certain causal Bayesian networks. We also show some preliminary results on the algebraic structure of polynomial constraints. The results have applications in distinguishing between causal models and in testing causal models with combined observational and experimental data.
Keywords:
Pages: 200-208
PS Link:
PDF Link: /papers/07/p200-kang.pdf
BibTex:
@INPROCEEDINGS{Kang07,
AUTHOR = "Changsung Kang and Jin Tian",
TITLE = "Polynomial Constraints in Causal Bayesian Networks",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "200--208"
}


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