IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v79y2009i4p537-542.html
   My bibliography  Save this article

A note on using the estimated versus the known propensity score to estimate the average treatment effect

Author

Listed:
  • Brumback, Babette A.

Abstract

We provide simple intuition why using the estimated versus known propensity score tends to increase, and never decreases, asymptotic efficiency. When a covariate is independent of response conditional on treatment, using the known score can have greater finite-sample efficiency.

Suggested Citation

  • Brumback, Babette A., 2009. "A note on using the estimated versus the known propensity score to estimate the average treatment effect," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 537-542, February.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:4:p:537-542
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(08)00463-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    References listed on IDEAS

    as
    1. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    2. Joshua Angrist & Jinyong Hahn, 2004. "When to Control for Covariates? Panel Asymptotics for Estimates of Treatment Effects," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 58-72, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Corwin Matthew Zigler, 2016. "The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 47-54, February.

    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. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    2. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    3. Atila Abdulkadiroğlu & Joshua D. Angrist & Yusuke Narita & Parag A. Pathak, 2017. "Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation," Econometrica, Econometric Society, vol. 85, pages 1373-1432, September.
    4. Joshua D. Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path From Cause to Effect," Econometrica, Econometric Society, vol. 90(6), pages 2509-2539, November.
    5. Basu, A & Polsky, D & Manning, W G, 2008. "Use of propensity scores in non-linear response models: The case for health care expenditures," Health, Econometrics and Data Group (HEDG) Working Papers 08/11, HEDG, c/o Department of Economics, University of York.
    6. Bryan S. Graham & Keisuke Hirano, 2011. "Robustness to Parametric Assumptions in Missing Data Models," American Economic Review, American Economic Association, vol. 101(3), pages 538-543, May.
    7. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    8. Ogrokhina, Olena & Rodriguez, Cesar M., 2018. "The role of inflation targeting in international debt denomination in developing countries," Journal of International Economics, Elsevier, vol. 114(C), pages 116-129.
    9. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    10. Rothe, Christoph, 2016. "The Value of Knowing the Propensity Score for Estimating Average Treatment Effects," IZA Discussion Papers 9989, Institute of Labor Economics (IZA).
    11. van de Walle, Dominique & Mu, Ren, 2007. "Fungibility and the flypaper effect of project aid: Micro-evidence for Vietnam," Journal of Development Economics, Elsevier, vol. 84(2), pages 667-685, November.
    12. Clément de Chaisemartin & Luc Behaghel, 2020. "Estimating the Effect of Treatments Allocated by Randomized Waiting Lists," Econometrica, Econometric Society, vol. 88(4), pages 1453-1477, July.
    13. Gunther Bensch & Jörg Peters, 2013. "Alleviating Deforestation Pressures? Impacts of Improved Stove Dissemination on Charcoal Consumption in Urban Senegal," Land Economics, University of Wisconsin Press, vol. 89(4), pages 676-698.
    14. Leonardo Becchetti & Pierluigi Conzo & Alessandro Romeo, 2014. "Violence, trust, and trustworthiness: evidence from a Nairobi slum," Oxford Economic Papers, Oxford University Press, vol. 66(1), pages 283-305, January.
    15. Futoshi Yamauchi & Yanyan Liu, 2013. "Impacts of an Early Stage Education Intervention on Students' Learning Achievement: Evidence from the Philippines," Journal of Development Studies, Taylor & Francis Journals, vol. 49(2), pages 208-222, February.
    16. Katie Meara & Francesco Pastore & Allan Webster, 2020. "The gender pay gap in the USA: a matching study," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(1), pages 271-305, January.
    17. Gernandt, Johannes & Maier, Michael & Pfeiffer, Friedhelm & Rat-Wirtzler, Julie, 2006. "Distributional effects of the high school degree in Germany," ZEW Discussion Papers 06-088, ZEW - Leibniz Centre for European Economic Research.
    18. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    19. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    20. Bodory, Hugo & Huber, Martin, 2018. "The causalweight package for causal inference in R," FSES Working Papers 493, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

    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:eee:stapro:v:79:y:2009:i:4:p:537-542. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.