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Confounding Equivalence in Causal Inference

Author

Listed:
  • 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)

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.

Suggested Citation

  • Pearl Judea & Paz Azaria, 2014. "Confounding Equivalence in Causal Inference," Journal of Causal Inference, De Gruyter, vol. 2(1), pages 1-19, March.
    • Handle: RePEc:bpj:causin:v:2:y:2014:i:1:p:19:n:3
    DOI: 10.1515/jci-2013-0020
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    References listed on IDEAS

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    1. Sander Greenland & Judea Pearl, 2011. "Adjustments and their Consequences—Collapsibility Analysis using Graphical Models," International Statistical Review, International Statistical Institute, vol. 79(3), pages 401-426, December.
    2. Manabu Kuroki & Masami Miyakawa, 2003. "Covariate selection for estimating the causal effect of control plans by using causal diagrams," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 209-222, February.
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