IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v2y2014i1p19n3.html
   My bibliography  Save this article

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 75-93, March.
    • Handle: RePEc:bpj:causin:v:2:y:2014:i:1:p:19:n:3
    DOI: 10.1515/jci-2013-0020
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2013-0020
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2013-0020?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Manabu Kuroki & Hisayoshi Nanmo, 2020. "Variance formulas for estimated mean response and predicted response with external intervention based on the back-door criterion in linear structural equation models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 667-685, December.
    2. F Richard Guo & Emilija Perković & Andrea Rotnitzky, 2023. "Variable elimination, graph reduction and the efficient g-formula," Biometrika, Biometrika Trust, vol. 110(3), pages 739-761.
    3. Leonard Henckel & Emilija Perković & Marloes H. Maathuis, 2022. "Graphical criteria for efficient total effect estimation via adjustment in causal linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 579-599, April.
    4. Sanjay Chaudhuri, 2014. "Qualitative inequalities for squared partial correlations of a Gaussian random vector," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 345-367, April.
    5. Yiming Chen & Paul J. Smith & Mei-Ling Ting Lee, 2023. "Causal Inference in Threshold Regression and the Neural Network Extension (TRNN)," Stats, MDPI, vol. 6(2), pages 1-24, April.
    6. Tyler J. VanderWeele & James M. Robins, 2010. "Signed directed acyclic graphs for causal inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 111-127, January.
    7. Carla Curado & Mírian Oliveira & Dara G. Schniederjans & Eduardo Kunzel Teixeira, 2024. "Control variable use and reporting in operations management: a systematic literature review and revisit," Management Review Quarterly, Springer, vol. 74(3), pages 1809-1839, September.
    8. Ryusei Shingaki & Hiroshi Kanda & Manabu Kuroki, 2021. "Selection and integration of generalized instrumental variables for estimating total effects," Statistical Papers, Springer, vol. 62(5), pages 2355-2381, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:causin:v:2:y:2014:i:1:p:19:n:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.