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Matching estimators of average treatment effects: a review applied to the evaluation of health care programmes

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  • Rodrigo Moreno-Serra

Abstract

The general aim of this paper is to review how matching methods try to solve the evaluation problem – with a particular focus on propensity score matching – and their usefulness for the particular case of health programme evaluation. The “classical” case of matching estimation with a single discrete treatment is presented as a basis for discussing recent developments concerning the application of matching methods for jointly evaluating the impact of multiple treatments and for evaluating the impact of a continuous treatment. For each case, I review the treatment effects parameters of interest, the required identification assumptions, the definition of the main matching estimators and their main theoretical properties and practical features. The relevance of the “classical” matching estimators and of their extensions for the multiple and continuous treatments settings is illustrated using the example of a health programme implemented with different levels of population coverage in different geographic areas.

Suggested Citation

  • Rodrigo Moreno-Serra, 2007. "Matching estimators of average treatment effects: a review applied to the evaluation of health care programmes," Health, Econometrics and Data Group (HEDG) Working Papers 07/02, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:07/02
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    File URL: https://www.york.ac.uk/media/economics/documents/herc/wp/07_02.pdf
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    References listed on IDEAS

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    1. Cameron,A. Colin & Trivedi,Pravin K., 2008. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9787111235767.
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    Cited by:

    1. de Preux, L, 2010. "Ex Ante Moral Hazard and Anticipatory Behaviour: Some Evidence," Health, Econometrics and Data Group (HEDG) Working Papers 10/13, HEDG, c/o Department of Economics, University of York.

    More about this item

    Keywords

    Evaluation methods; treatment effects; matching; propensity score; programme evaluation.;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • I10 - Health, Education, and Welfare - - Health - - - General

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