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Simulation-based sensitivity analysis for matching estimators

  • Tommaso Nannicini


    (Universidad Carlos III de Madrid)

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.

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Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 7 (2007)
Issue (Month): 3 (September)
Pages: 334-350

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Handle: RePEc:tsj:stataj:v:7:y:2007:i:3:p:334-350
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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 604-20, September.
  2. 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.
  3. 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.
  4. Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
  5. Barbara Sianesi, 2001. "Propensity score matching," United Kingdom Stata Users' Group Meetings 2001 12, Stata Users Group, revised 23 Aug 2001.
  6. 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.
  7. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2000. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," NBER Working Papers 7831, National Bureau of Economic Research, Inc.
  8. Ichino, Andrea & Mealli, Fabrizia & Nannicini, Tommaso, 2006. "From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and their Sensitivity?," IZA Discussion Papers 2149, Institute for the Study of Labor (IZA).
  9. 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.
  10. Guildo W. Imbens, 2003. "Sensitivity to Exogeneity Assumptions in Program Evaluation," American Economic Review, American Economic Association, vol. 93(2), pages 126-132, May.
  11. 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.
  12. 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.
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