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mhbounds - Sensitivity Analysis for Average Treatment Effects

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Author Info
Sascha O. Becker () (CES, University of Munich and IZA)
Marco Caliendo () (DIW Berlin and IZA)

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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.

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Publisher Info
Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 2542.

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Length: 15 pages
Date of creation: Jan 2007
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Handle: RePEc:iza:izadps:dp2542

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Related research
Keywords: matching; treatment effects; sensitivity analysis; unobserved heterogeneity;

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Find related papers by JEL classification:
C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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References listed on IDEAS
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  1. 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.
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  2. Marco Caliendo & Sabine Kopeinig, 2005. "Some Practical Guidance for the Implementation of Propensity Score Matching," IZA Discussion Papers 1588, Institute for the Study of Labor (IZA). [Downloadable!]
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  3. 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. [Downloadable!] (restricted)
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  4. repec:att:wimass:199217 is not listed on IDEAS
  5. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
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  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. [Downloadable!]
  8. 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. [Downloadable!]
  9. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September. [Downloadable!]
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