Uncertainty in Artificial Intelligence
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Inequality Constraints in Causal Models with Hidden Variables
Changsung Kang, Jin Tian
Abstract:
We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. We derive instrumental inequality type of constraints on nonexperimental distributions. The results have applications in testing causal models with observational or experimental data.
Keywords:
Pages: 233-240
PS Link:
PDF Link: /papers/06/p233-kang.pdf
BibTex:
@INPROCEEDINGS{Kang06,
AUTHOR = "Changsung Kang and Jin Tian",
TITLE = "Inequality Constraints in Causal Models with Hidden Variables",
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 = "233--240"
}


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