IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v6y2010i2n11.html
   My bibliography  Save this article

Selective Ignorability Assumptions in Causal Inference

Author

Listed:
  • Joffe Marshall M.

    (University of Pennsylvania School of Medicine)

  • Yang Wei Peter

    (University of Pennsylvania School of Medicine)

  • Feldman Harold I.

    (University of Pennsylvania School of Medicine)

Abstract

Most attempts at causal inference in observational studies are based on assumptions that treatment assignment is ignorable. Such assumptions are usually made casually, largely because they justify the use of available statistical methods and not because they are truly believed. It will often be the case that it is plausible that conditional independence holds at least approximately for a subset but not all of the experience giving rise to one's data. Such selective ignorability assumptions may be used to derive valid causal inferences in conjunction with structural nested models. In this paper, we outline selective ignorability assumptions mathematically and sketch how they may be used along with otherwise standard G-estimation or likelihood-based methods to obtain inference on structural nested models. We also consider use of these assumptions in the presence of selective measurement error or missing data when the missingness is not at random. We motivate and illustrate our development by considering an analysis of an observational database to estimate the effect of erythropoietin use on mortality among hemodialysis patients.

Suggested Citation

  • Joffe Marshall M. & Yang Wei Peter & Feldman Harold I., 2010. "Selective Ignorability Assumptions in Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-25, March.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:11
    DOI: 10.2202/1557-4679.1199
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1199
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1199?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marshall M. Joffe & Wei Peter Yang & Harold Feldman, 2012. "G-Estimation and Artificial Censoring: Problems, Challenges, and Applications," Biometrics, The International Biometric Society, vol. 68(1), pages 275-286, March.
    2. Judea Pearl, 2012. "Bias and Causation, Models and Judgment for Valid Comparisons by WEISBERG, H. I," Biometrics, The International Biometric Society, vol. 68(2), pages 659-660, June.
    3. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.

    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:ijbist:v:6:y:2010:i:2:n:11. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.