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Regression modelQuasi-experimental / causal inference

Mašinsko učenje-augmentovano razlika-u-razlici (ML-DiD)

ML-DiD kombinuje klasičnu strategiju identifikacije razlika-u-razlici sa fleksibilnim ML proceniteljima za pomoćne funkcije — sklonost ka tretmanu i regresiju ishoda — kako bi se dobile validne kauzalne procene čak i kada su složenost izbora tretmana i dinamika ishoda kompleksne, visokodimenzionalne ili nelinearne. Pristup, ukorenjen u dvostruko/debiased mašinsko učenje (Chernozhukov et al., 2018) i dvostruko-robusno DiD (Sant'Anna & Zhao, 2020), štiti od pristrasnosti pogrešne specifikacije, istovremeno čuvajući osnovnu logiku DiD poređenja pre-posle, tretirani-u-odnosu-na-kontrolu.

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Izvori

  1. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI: 10.1111/ectj.12097
  2. Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI: 10.1016/j.jeconom.2020.12.001

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Machine Learning-Augmented Difference-in-Differences Estimator. ScholarGate. https://scholargate.app/sr/causal-inference/machine-learning-augmented-difference-in-differences

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Citirana u

ScholarGateMachine learning-augmented difference-in-differences (Machine Learning-Augmented Difference-in-Differences Estimator). Preuzeto 2026-06-15 sa https://scholargate.app/sr/causal-inference/machine-learning-augmented-difference-in-differences · Skup podataka: https://doi.org/10.5281/zenodo.20539026