Kausalanalyse durch Matchingverfahren

Markus Gangl and Thomas A. DiPrete

No 401, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

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.

Keywords: Matching; Causality; Nonparametric estimators; Observational data; Rubin causal model; Counterfactual analysis (search for similar items in EconPapers)
Pages: 30 p.
Date: 2004
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Citations: View citations in EconPapers (2)

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