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mhbounds - Sensitivity Analysis for Average Treatment Effects

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  • Becker, Sascha O.

    (Monash University)

  • Caliendo, Marco

    (University of Potsdam)

Abstract

Matching has become a popular approach to estimate average treatment effects. It is based on the conditional independence or unconfoundedness assumption. Checking the sensitivity of the estimated results with respect to deviations from this identifying assumption has become an increasingly important topic in the applied evaluation literature. If there are unobserved variables which affect assignment into treatment and the outcome variable simultaneously, a hidden bias might arise to which matching estimators are not robust. We address this problem with the bounding approach proposed by Rosenbaum (2002), where mhbounds allows the researcher to determine how strongly an unmeasured variable must influence the selection process in order to undermine the implications of the matching analysis.

Suggested Citation

  • Becker, Sascha O. & Caliendo, Marco, 2007. "mhbounds - Sensitivity Analysis for Average Treatment Effects," IZA Discussion Papers 2542, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp2542
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    References listed on IDEAS

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    1. 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.
    2. Manski, Charles F., 1992. "Identification Problems In The Social Sciences," SSRI Workshop Series 292716, University of Wisconsin-Madison, Social Systems Research Institute.
    3. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September.
    4. 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.
    5. DiPrete, Thomas A. & Gangl, Markus, 2004. "Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments," Discussion Papers, Research Unit: Labor Market Policy and Employment SP I 2004-101, WZB Berlin Social Science Center.
    6. 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.
    7. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    8. Marco Caliendo & Reinhard Hujer & Stephan L. Thomsen, 2008. "The employment effects of job-creation schemes in Germany: A microeconometric evaluation," Advances in Econometrics, in: Modelling and Evaluating Treatment Effects in Econometrics, pages 381-428, Emerald Group Publishing Limited.
    9. 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.
    10. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    11. 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.
    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.
    13. 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.
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    More about this item

    Keywords

    unobserved heterogeneity; sensitivity analysis; treatment effects; matching;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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