IDEAS home Printed from https://ideas.repec.org/p/cca/wplabo/169.html
   My bibliography  Save this paper

PSMatching: A SAS Macro for Treatment Effect Estimation and Missing Data Imputation Based on Propensity Score Matching

Author

Listed:
  • Roberto Leombruni
  • Michele Mosca

Abstract

Matching estimators based on the propensity score are widely used in the field of treatment effect evaluation and a viable technique also for missing data imputation. This paper describes an implementation of the technique in SAS®, a statistical software where only limited implementations of it are currently available. The user can choose among the most common variants of the matching algorithms (nearest neighbour-, stratification- and kernel matching), and the main pre- and post estimation analyses proposed in the literature (reduction of standardised bias, Sianesi test, Ichino-Becker test on the balancing hypotheses, Lechner bounds). To these, two additional diagnostics are proposed in order to better monitor some aspects of the matching process. A validation of the procedure is made using the available STATA tools as a benchmark, with both artificial data and data already used in the literature.

Suggested Citation

  • Roberto Leombruni & Michele Mosca, 2019. "PSMatching: A SAS Macro for Treatment Effect Estimation and Missing Data Imputation Based on Propensity Score Matching," LABORatorio R. Revelli Working Papers Series 169, LABORatorio R. Revelli, Centre for Employment Studies.
    • Handle: RePEc:cca:wplabo:169
    as

    Download full text from publisher

    File URL: http://www.laboratoriorevelli.it/_pdf/wp169.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Galizzi, Monica & Leombruni, Roberto & Pacelli, Lia, 2023. "Severe work disabilities and long-lasting losses," Labour Economics, Elsevier, vol. 85(C).

    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:cca:wplabo:169. 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: Giovanni Bert (email available below). General contact details of provider: https://edirc.repec.org/data/fccaait.html .

    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.