Uncertainty in Artificial Intelligence
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Confounding Equivalence in Causal Inference
Judea Pearl, Azaria Paz
Abstract:
The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test re- quires that one of the following two condi- tions holds: either (1) both sets are admis- sible (i.e., satisfy the back-door criterion) or (2) the Markov boundaries surrounding the manipulated variable(s) are identical in both sets. Applications to covariate selection and model testing are discussed.
Keywords:
Pages: 433-441
PS Link:
PDF Link: /papers/10/p433-pearl.pdf
BibTex:
@INPROCEEDINGS{Pearl10,
AUTHOR = "Judea Pearl and Azaria Paz",
TITLE = "Confounding Equivalence in Causal Inference",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "433--441"
}


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