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| Машинно обучение-разширен метод на разликите в разликите (ML-DiD)× | Метод на разликите в разликите (Difference-in-Differences, DiD)× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2018-2020 | 1994 |
| Създател≠ | Chernozhukov et al. (double/debiased ML framework); Sant'Anna & Zhao (2020) for DR-DiD | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Тип≠ | Causal inference / semiparametric | Causal inference / panel regression |
| Основополагащ източник≠ | 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 ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Други названия≠ | ML-DiD, double/debiased ML DiD, DML difference-in-differences, augmented DiD | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Свързани≠ | 6 | 5 |
| Резюме≠ | Machine learning-augmented DiD combines the classic difference-in-differences identification strategy with flexible ML estimators for nuisance functions — the propensity score and the outcome regression — to obtain valid causal estimates even when treatment selection and outcome dynamics are complex, high-dimensional, or nonlinear. The approach, rooted in double/debiased machine learning (Chernozhukov et al., 2018) and doubly-robust DiD (Sant'Anna & Zhao, 2020), guards against misspecification bias while preserving the core DiD logic of before-after, treated-versus-control comparisons. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
| ScholarGateНабор от данни ↗ |
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