Matching estimators of average treatment effects: a review applied to the evaluation of health care programmes
AbstractThe 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 07/02.
Date of creation: Feb 2007
Date of revision:
Contact details of provider:
Postal: HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/res/herc/research/hedg/
More information through EDIRC
Evaluation methods; treatment effects; matching; propensity score; programme evaluation.;
Find related papers by 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; Longitudinal Data; Spatial Time Series
- I10 - Health, Education, and Welfare - - Health - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-09-30 (All new papers)
- NEP-ECM-2007-09-30 (Econometrics)
- NEP-HEA-2007-09-30 (Health Economics)
- NEP-PPM-2007-09-30 (Project, Program & Portfolio Management)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statistics
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jane Rawlings).
If references are entirely missing, you can add them using this form.