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Propensity Scores

Author

Listed:
  • Jason K. Luellen

    (University of Memphis, jluellen@memphis.edu)

  • William R. Shadish

    (University of California, Merced)

  • M. H. Clark

    (Southern Illinois University)

Abstract

Propensity score analysis is a relatively recent statistical innovation that is useful in the analysis of data from quasi-experiments. The goal of propensity score analysis is to balance two non-equivalent groups on observed covariates to get more accurate estimates of the effects of a treatment on which the two groups differ. This article presents a general introduction to propensity score analysis, provides an example using data from a quasi-experiment compared to a benchmark randomized experiment, offers practical advice about how to do such analyses, and discusses some limitations of the approach. It also presents the first detailed instructions to appear in the literature on how to use classification tree analysis and bagging for classification trees in the construction of propensity scores. The latter two examples serve as an introduction for researchers interested in computing propensity scores using more complex classification algorithms known as ensemble methods.

Suggested Citation

  • Jason K. Luellen & William R. Shadish & M. H. Clark, 2005. "Propensity Scores," Evaluation Review, , vol. 29(6), pages 530-558, December.
    • Handle: RePEc:sae:evarev:v:29:y:2005:i:6:p:530-558
    DOI: 10.1177/0193841X05275596
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    References listed on IDEAS

    as
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    3. repec:mpr:mprres:3694 is not listed on IDEAS
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    Cited by:

    1. Maureen A. Pirog & Anne L. Buffardi & Colleen K. Chrisinger & Pradeep Singh & John Briney, 2009. "Are the alternatives to randomized assignment nearly as good? Statistical corrections to nonrandomized evaluations," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 169-172.
    2. Elert, Niklas & Andersson, Fredrik W. & Wennberg, Karl, 2015. "The impact of entrepreneurship education in high school on long-term entrepreneurial performance," Journal of Economic Behavior & Organization, Elsevier, vol. 111(C), pages 209-223.
    3. Emanuela Galasso & Nithin Umapathi, 2009. "Improving nutritional status through behavioural change: lessons from Madagascar," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 1(1), pages 60-85.
    4. Lars Skipper & Marianne Simonsen, 2006. "The costs of motherhood: an analysis using matching estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 919-934.
    5. Millimet, Daniel L. & Tchernis, Rusty, 2009. "On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 397-415.

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