Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ причинно-следственного влияния на панельных данных× | Метод прерванного временного ряда на панельных данных× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2015 (base method); panel extension mid-2010s | 2000s–2010s |
| Автор метода≠ | Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature | Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial) |
| Тип≠ | Bayesian structural time-series causal inference | Quasi-experimental causal inference |
| 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 ↗ | Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ |
| Другие названия | Panel CausalImpact, multi-unit causal impact, panel BSTS causal inference, panel structural time-series causal analysis | panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Panel data causal impact analysis extends the Bayesian structural time-series approach of Brodersen et al. (2015) to multi-unit panel settings, estimating the counterfactual for several treated units simultaneously using control units as a donor pool. It produces credible intervals for the causal effect at each post-intervention time point, aggregated across units and periods. | Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detecting changes in the level and slope of the outcome trajectory immediately following a clearly dated intervention. |
| ScholarGateНабор данных ↗ |
|
|