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Sensitivity analysis for average treatment effects

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Author Info

  • Sascha O. Becker

    ()
    (Center for Economic Studies, Ludwig-Maximilians-University)

  • Marco Caliendo

    ()
    (DIW-Berlin)

Abstract

Based on the conditional independence or unconfoundedness assump- tion, matching has become a popular approach to estimate average treatment effects. Checking the sensitivity of the estimated results with respect to devia- tions from this identifying assumption has become an increasingly important topic in the applied evaluation literature. If there are unobserved variables that affect assignment into treatment and the outcome variable simultaneously, a hidden bias might arise to which matching estimators are not robust. We address this prob- lem with the bounding approach proposed by Rosenbaum (Observational Studies, 2nd ed., New York: Springer), where mhbounds lets the researcher determine how strongly an unmeasured variable must influence the selection process to undermine the implications of the matching analysis. Copyright 2007 by StataCorp LP.

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Bibliographic Info

Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 7 (2007)
Issue (Month): 1 (February)
Pages: 71-83

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Handle: RePEc:tsj:stataj:v:7:y:2007:i:1:p:71-83

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Related research

Keywords: mhbounds; matching; treatment effects; sensitivity analysis; unobserved heterogeneity; Rosenbaum bounds;

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References

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  1. Caliendo, Marco & Hujer, Reinhard & Thomsen, Stephan Lothar, 2005. "The Employment Effects of Job Creation Schemes in Germany: A Microeconometric Evaluation," IZA Discussion Papers 1512, Institute for the Study of Labor (IZA).
  2. 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.
  3. Marco Caliendo & Sabine Kopeinig, 2005. "Some Practical Guidance for the Implementation of Propensity Score Matching," Discussion Papers of DIW Berlin 485, DIW Berlin, German Institute for Economic Research.
  4. 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.
  5. repec:att:wimass:8909 is not listed on IDEAS
  6. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May.
  7. 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.
  8. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September.
  9. 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.
  10. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
  11. repec:att:wimass:9217 is not listed on IDEAS
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