Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский метод синтетического контроля× | Байесовский метод разности разностей (Bayesian Difference-in-Differences)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal) | 2015-2023 |
| Автор метода≠ | Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller | Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series) |
| Тип≠ | Bayesian causal inference / synthetic control | Bayesian causal inference / panel regression |
| Основополагающий источник≠ | Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274. DOI ↗ | Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529. link ↗ |
| Другие названия | Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC | Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator |
| Связанные | 5 | 5 |
| Сводка≠ | The Bayesian Synthetic Control Method estimates the causal effect of an intervention on a single treated unit by constructing a probabilistic counterfactual from a weighted combination of untreated donor units. Unlike the classical SCM, it places a prior distribution over the synthetic weights, yielding full posterior uncertainty intervals for the counterfactual trajectory and the treatment effect at each post-intervention time point. | Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available. |
| ScholarGateНабор данных ↗ |
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