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Simulation-based sensitivity analysis for matching estimators

Author

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  • Tommaso Nannicini

    (Universidad Carlos III de Madrid)

Abstract

This article presents a Stata program (sensatt) that implements the sensitivity analysis for matching estimators proposed by Ichino, Mealli, and Nannicini (Journal of Applied Econometrics , forthcoming). The analysis simulates a potential confounder to assess the robustness of the estimated treatment effects with respect to deviations from the conditional independence assumption. The program uses the commands for propensity-score matching (att* ) developed by Becker and Ichino (Stata Journal 2: 358–377). I give an example by using the National Supported Work demonstration, widely known in the program evaluation literature. Copyright 2007 by StataCorp LP.

Suggested Citation

  • Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September.
    • Handle: RePEc:tsj:stataj:v:7:y:2007:i:3:p:334-350
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    References listed on IDEAS

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    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
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    4. Andrea Ichino & Fabrizia Mealli & Tommaso Nannicini, 2008. "From temporary help jobs to permanent employment: what can we learn from matching estimators and their sensitivity?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 305-327.
    5. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
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    8. Barbara Sianesi, 2001. "Propensity score matching," United Kingdom Stata Users' Group Meetings 2001 12, Stata Users Group, revised 23 Aug 2001.
    9. Sascha O. Becker & Marco Caliendo, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LP, vol. 7(1), pages 71-83, February.
    10. 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.
    11. 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, February.
    12. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
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    15. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
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    17. 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.
    18. 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 01 Feb 2018.
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