IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v54y2025i15p4896-4908.html
   My bibliography  Save this article

Estimation of a treatment effect by linear regression, classical stratification and stratification on the propensity score: a comparison in the case of discrete covariates

Author

Listed:
  • José A. Ferreira

Abstract

This paper provides a self-contained introduction to the methods of classical stratification and stratification on the propensity score for students of linear regression and more generally for applied statisticians who have not yet made up their minds about the relative worth of these methods. By considering only discrete regressors and by taking into account what is known about the distribution of the data, it is possible to make simple statements about consistency, unbiasedness and efficiency and to provide some perspective on the relative merits of the three methods.

Suggested Citation

  • José A. Ferreira, 2025. "Estimation of a treatment effect by linear regression, classical stratification and stratification on the propensity score: a comparison in the case of discrete covariates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(15), pages 4896-4908, August.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:15:p:4896-4908
    DOI: 10.1080/03610926.2024.2430737
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2024.2430737
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2024.2430737?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:lstaxx:v:54:y:2025:i:15:p:4896-4908. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.