Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Оценка политики: Анализ причинно-следственного воздействия× | Анализ прерванных временных рядов (Interrupted Time Series, ITS)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2015 | 2002 |
| Автор метода≠ | Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Тип≠ | Bayesian counterfactual / time-series | Quasi-experimental segmented 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 ↗ | Bernal, J. L., 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≠ | policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Policy Evaluation Causal Impact Analysis applies the Bayesian structural time-series (BSTS) framework of Brodersen et al. (2015) to estimate the causal effect of a policy intervention on aggregate outcomes. By constructing a synthetic counterfactual from pre-policy data and control covariates, it asks: what would have happened had the policy not been enacted? The difference between observed and predicted post-policy outcomes is the estimated policy effect. | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. |
| ScholarGateНабор данных ↗ |
|
|