Confounding Equivalence in Causal Inference

Pearl Judea () and Paz Azaria ()
Additional contact information
Pearl Judea: Department of Computer Science, University of California – Los Angeles, Los Angeles, CA 90095-1596 USA
Paz Azaria: Department of Computer Science, Technion IIT, Haifa 3200, Israel

Journal of Causal Inference, 2014, vol. 2, issue 1, 75-93

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 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.

Keywords: confounding; model testing; selection bias; extended back-door; covariate selection (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2013-0020 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:2:y:2014:i:1:p:19:n:3

DOI: 10.1515/jci-2013-0020

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().