Uncertainty in Artificial Intelligence
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On the Testable Implications of Causal Models with Hidden Variables
Jin Tian, Judea Pearl
Abstract:
The validity OF a causal model can be tested ONLY IF the model imposes constraints ON the probability distribution that governs the generated data. IN the presence OF unmeasured variables, causal models may impose two types OF constraints : conditional independencies, AS READ through the d - separation criterion, AND functional constraints, FOR which no general criterion IS available.This paper offers a systematic way OF identifying functional constraints AND, thus, facilitates the task OF testing causal models AS well AS inferring such models FROM data.
Keywords:
Pages: 519-527
PS Link:
PDF Link: /papers/02/p519-tian.pdf
BibTex:
@INPROCEEDINGS{Tian02,
AUTHOR = "Jin Tian and Judea Pearl",
TITLE = "On the Testable Implications of Causal Models with Hidden Variables",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "519--527"
}


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