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Kausalanalyse durch Matchingverfahren

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  • Markus Gangl
  • Thomas A. DiPrete

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.

Suggested Citation

  • Markus Gangl & Thomas A. DiPrete, 2004. "Kausalanalyse durch Matchingverfahren," Discussion Papers of DIW Berlin 401, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp401
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    References listed on IDEAS

    as
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