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Estimation of Causal Effects with a Binary Treatment Variable: A Unified M-Estimation Framework

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  • Uysal Derya

    (Department of Economics, University of Munich, Munich, Germany)

Abstract

In this paper, we review several estimators of the average treatment effect (ATE) that belong to three main groups: regression, weighting and doubly robust methods. We unify the exposition of these estimators within an M-estimation framework and we derive their variance estimators from the sandwich form variance-covariance matrix of the M-Estimator. Additionally, we re-estimate the causal return to higher education on earnings by the reviewed methods using the rich dataset provided by the British National Child Development Study (NCDS) as an empirical illustration.

Suggested Citation

  • Uysal Derya, 2024. "Estimation of Causal Effects with a Binary Treatment Variable: A Unified M-Estimation Framework," Journal of Econometric Methods, De Gruyter, vol. 13(1), pages 145-204, January.
    • Handle: RePEc:bpj:jecome:v:13:y:2024:i:1:p:145-204:n:1
    DOI: 10.1515/jem-2020-0021
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    More about this item

    Keywords

    ATE; M-estimation; treatment effects; double robustness;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect 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

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