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A Simulation-Based Sensitivity Analysis for Matching Estimators

  • Tommaso Nannicini

    ()

    (Universidad Carlos III de Madrid)

This paper presents a Stata program ("sensatt") that implements the sensitivity analysis for propensity-score matching estimators proposed by Ichino, Mealli and Nannicini (2005). The proposed analysis builds on Rosenbaum and Rubin (1983) and Rosenbaum (1987), and simulates a potential confounder in order to assess the robustness of the estimated treatment effect with respect to specific deviations from the Conditional Independence Assumption (CIA). The program "sensatt" makes use of the user-written Stata commands for propensity-score matching estimation (att*) developed by Becker and Ichino (2002). An example of the implementation of the proposed sensitivity analysis is given by using the National Supported Work (NSW) demonstration, widely known in the program evaluation literature.

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Paper provided by Stata Users Group in its series North American Stata Users' Group Meetings 2006 with number 6.

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Date of creation: 23 Jul 2006
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Handle: RePEc:boc:asug06:6
Contact details of provider: Web page: http://www.stata.com/meeting/5nasug

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  1. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
  2. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
  3. 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.
  4. Edwin Leuven & Barbara Sianesi, 2003. "PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing," Statistical Software Components S432001, Boston College Department of Economics, revised 19 Jan 2015.
  5. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, 02.
  6. Charles Michalopoulos & Howard S. Bloom & Carolyn J. Hill, 2004. "Can Propensity-Score Methods Match the Findings from a Random Assignment Evaluation of Mandatory Welfare-to-Work Programs?," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 156-179, February.
  7. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
  8. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
  9. Ichino, Andrea & Mealli, Fabrizia & Nannicini, Tommaso, 2006. "From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and their Sensitivity?," CEPR Discussion Papers 5736, C.E.P.R. Discussion Papers.
  10. Barbara Sianesi, 2001. "Propensity score matching," United Kingdom Stata Users' Group Meetings 2001 12, Stata Users Group, revised 23 Aug 2001.
  11. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
  12. Guildo W. Imbens, 2003. "Sensitivity to Exogeneity Assumptions in Program Evaluation," American Economic Review, American Economic Association, vol. 93(2), pages 126-132, May.
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