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Matching Estimators of Causal Effects
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Matching Estimators of Causal Effects

Author

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  • Stephen L. Morgan

    (Cornell University, Ithaca, NY)

  • David J. Harding

    (University of Michigan, Ann Arbor)

Abstract

As the counterfactual model of causality has increased in popularity, sociologists have returned to matching as a research methodology. In this article, advances over the past two decades in matching estimators are explained, and the practical limitations of matching techniques are emphasized. The authors introduce matching methods by focusing first on ideal scenarios in which stratification and weighting procedures warrant causal inference. Then, they discuss how matching is often undertaken in practice, offering an overview of the most prominent data analysis routines. With four hypothetical examples, they demonstrate how the assumptions behind matching estimators often break down in practice. Even so, the authors argue that matching techniques can be used effectively to strengthen the prosecution of causal questions in sociology.

Suggested Citation

  • Stephen L. Morgan & David J. Harding, 2006. "Matching Estimators of Causal Effects," Sociological Methods & Research, , vol. 35(1), pages 3-60, August.
    • Handle: RePEc:sae:somere:v:35:y:2006:i:1:p:3-60
    DOI: 10.1177/0049124106289164
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    References listed on IDEAS

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    11. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
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    16. Michael Lechner, 2002. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 59-82, February.
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