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Kausalanalyse durch Matchingverfahren

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  • Markus Gangl
  • Thomas A. DiPrete
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    Abstract

    Having close linkages with the counterfactual concept of causality, nonparametric matching estimators have recently gained in popularity in the statistical and econometric literature on causal analysis. Introducing key concepts of the Rubin causal model (RCM), the paper discusses the implementation of counterfactual analyses by propensity score matching methods. We emphasize the suitability of the counterfactual framework for sociological questions as well as the assumptions underlying matching methods relative to standard regression analysis. We then illustrate the application of matching estimators in an analysis of the causal effect of unemployment on workers' subsequent careers.

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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.41226.de/dp401.pdf
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    Bibliographic Info

    Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 401.

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    Length: 30 p.
    Date of creation: 2004
    Date of revision:
    Handle: RePEc:diw:diwwpp:dp401

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    Related research

    Keywords: Matching; Causality; Nonparametric estimators; Observational data; Rubin causal model; Counterfactual analysis;

    References

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    1. 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.
    2. Heckman, James J. & Navarro, Salvador, 2003. "Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models," IZA Discussion Papers 768, Institute for the Study of Labor (IZA).
    3. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
    4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, Econometric Society, vol. 62(2), pages 467-75, March.
    5. Ernst Fehr & Simon Gaechter, . "Cooperation and Punishment in Public Goods Experiments," IEW - Working Papers 010, Institute for Empirical Research in Economics - University of Zurich.
    6. James J. Heckman, 1991. "Randomization and Social Policy Evaluation," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    7. Heckman, James, 2001. "Accounting for Heterogeneity, Diversity and General Equilibrium in Evaluating Social Programmes," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 111(475), pages F654-99, November.
    8. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
    9. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, Elsevier, vol. 30(1-2), pages 239-267.
    10. James Heckman & Justin L. Tobias & Edward Vytlacil, 2003. "Simple Estimators for Treatment Parameters in a Latent-Variable Framework," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 748-755, August.
    11. Falk, Armin & Fehr, Ernst, 2003. "Why labour market experiments?," Labour Economics, Elsevier, Elsevier, vol. 10(4), pages 399-406, August.
    12. 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, Social Science Research Center Berlin (WZB).
    13. Pratt, John W. & Schlaifer, Robert, 1988. "On the interpretation and observation of laws," Journal of Econometrics, Elsevier, Elsevier, vol. 39(1-2), pages 23-52.
    14. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 305-353.
    15. Lechner, Michael, 1999. "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 17(1), pages 74-90, January.
    16. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 65(2), pages 261-94, April.
    17. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
    18. Manski, C.F., 1990. "The Selection Problem," Working papers, Wisconsin Madison - Social Systems 90-12, Wisconsin Madison - Social Systems.
    19. Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
    20. repec:att:wimass:9217 is not listed on IDEAS
    21. Petra E. Todd & Jeffrey A. Smith, 2001. "Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods," American Economic Review, American Economic Association, American Economic Association, vol. 91(2), pages 112-118, May.
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