Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская проверка на плацебо× | Байесовский метод разности разностей (Bayesian Difference-in-Differences)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2010-2015 | 2015-2023 |
| Автор метода≠ | Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition) | Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series) |
| Тип≠ | Robustness check / falsification test | 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 falsification test, Bayesian permutation placebo, Bayesian robustness check, Bayesian in-time placebo | Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator |
| Связанные | 5 | 5 |
| Сводка≠ | The Bayesian Placebo Test is a falsification strategy for causal inference that applies Bayesian inference to placebo scenarios — either fake treatments in the pre-intervention period, on unaffected units, or at fictitious cut-offs — to verify that observed treatment effects cannot plausibly arise by chance or from a misspecified model. It integrates prior information and yields posterior distributions of placebo effects for direct probabilistic comparison. | 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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