Uncertainty in Artificial Intelligence
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Generalized Instrumental Variables
Carlos Brito, Judea Pearl
Abstract:
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Variables, which allows its application to models with few conditional independeces.
Keywords:
Pages: 85-93
PS Link:
PDF Link: /papers/02/p85-brito.pdf
BibTex:
@INPROCEEDINGS{Brito02,
AUTHOR = "Carlos Brito and Judea Pearl",
TITLE = "Generalized Instrumental 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 = "85--93"
}


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