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An introduction to matching methods for causal inference and their implementation in Stata

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  • Barbara Sianesi

    (Institute for Fiscal Studies, London)

Abstract

Matching, especially in its propensity-score flavors, has become an extremely popular evaluation method. Matching is, in fact, the best-available method for selecting a matched (or reweighted) comparison group that looks like the treatment group of interest. In this talk, I will introduce matching methods within the general problem of causal inference, highlight their strengths and weaknesses, and offer a brief overview of different matching estimators. Using psmatch2, I will then step through a practical example in Stata that is based on real data. I will then show how to implement some of these estimators, as well as highlight a number of implementational issues.

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Bibliographic Info

Paper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2010 with number 13.

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Date of creation: 17 Sep 2010
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Handle: RePEc:boc:usug10:13

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Web page: http://www.stata.com/meeting/uk10
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  1. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
  2. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, 01.
  3. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
  4. 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, 07.
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