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The Estimation of Causal Effects by Difference-in-Difference Methods

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  • Michael Lechner

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Abstract

This survey gives a brief overview of the literature on the difference-in-difference (DiD) estimation strategy and discusses major issues using a treatment effect perspective. In this sense, this survey gives a somewhat different view on DiD than the standard textbook discussion of the difference-in-difference model, but it will also not be as complete as the latter. This survey contains also a couple of extensions to the literature, for example, a discussion of and suggestions for non-linear DiD as well as DiD based on propensity-score type matching methods.

Suggested Citation

  • Michael Lechner, 2010. "The Estimation of Causal Effects by Difference-in-Difference Methods," University of St. Gallen Department of Economics working paper series 2010 2010-28, Department of Economics, University of St. Gallen, revised Oct 2011.
    • Handle: RePEc:usg:dp2010:2010-28
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    References listed on IDEAS

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    More about this item

    Keywords

    Causal inference; counterfactual analysis; before-after-treatment-control design; control group design with pretest and posttest;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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