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

S. Derya Uysal

Journal of Econometric Methods2024https://doi.org/10.1515/jem-2020-0021article
AJG 1ABDC B
Weight
0.46

What the paper says

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.

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https://doi.org/https://doi.org/10.1515/jem-2020-0021

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@article{s.2024,
  title        = {{Estimation of Causal Effects with a Binary Treatment Variable: A Unified M-Estimation Framework}},
  author       = {S. Derya Uysal},
  journal      = {Journal of Econometric Methods},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1515/jem-2020-0021},
}

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

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.38 × 0.4 = 0.15
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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