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Media type:
E-Article
Title:
Confounding Equivalence in Causal Inference
Contributor:
Pearl, Judea;
Paz, Azaria
imprint:
Walter de Gruyter GmbH, 2014
Published in:Journal of Causal Inference
Language:
Not determined
DOI:
10.1515/jci-2013-0020
ISSN:
2193-3677;
2193-3685
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>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 requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the back-door criterion) or (2) the Markov boundaries surrounding the treatment variable are identical in both sets. We further extend the test to include treatment-dependent covariates by broadening the back-door criterion and establishing equivalence of adjustment under selection bias conditions. Applications to covariate selection and model testing are discussed.</jats:p>