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Selection of Control Variables in Propensity Score Matching: Evidence from a Simulation Study

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  • Nguyen Viet, Cuong

Abstract

Propensity score matching is a widely-used method to measure the effect of a treatment in social as well as health sciences. An important issue in propensity score matching is how to select conditioning variables in estimation of the propensity score. It is commonly mentioned that only variables which affect both program participation and outcomes are selected. Using Monte Carlo simulation, this paper shows that efficiency in estimation of the Average Treatment Effect on the Treated can be gained if all the available observed variables in the outcome equation are included in the estimation of the propensity score.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 36377.

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Date of creation: 10 Feb 2012
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Handle: RePEc:pra:mprapa:36377

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Keywords: Impact evaluation; treatment effect; propensity score matching; covariate selection; Monte Carlo;

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  1. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 305-353.
  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. Alex Bryson & Richard Dorsett & Susan Purdon, 2002. "The use of propensity score matching in the evaluation of active labour market policies," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library 4993, London School of Economics and Political Science, LSE Library.
  4. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers, Econometric Society 1166, Econometric Society.
  5. Michael Lechner, 2005. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Labor and Demography, EconWPA 0505006, EconWPA.
  6. Alberto Abadie & Guido W. Imbens, 2006. "On the Failure of the Bootstrap for Matching Estimators," NBER Technical Working Papers 0325, National Bureau of Economic Research, Inc.
  7. Augurzky, Boris & Schmidt, Christoph M., 2001. "The Propensity Score: A Means to An End," IZA Discussion Papers 271, Institute for the Study of Labor (IZA).
  8. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, Econometric Society, vol. 66(5), pages 1017-1098, September.
  9. Cuong, Nguyen Viet, 2009. "Impact evaluation of multiple overlapping programs under a conditional independence assumption," Research in Economics, Elsevier, Elsevier, vol. 63(1), pages 27-54, March.
  10. Zhao, Zhong, 2005. "Sensitivity of Propensity Score Methods to the Specifications," IZA Discussion Papers 1873, Institute for the Study of Labor (IZA).
  11. 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, Wiley Blackwell, vol. 64(4), pages 605-54, October.
  12. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
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