Methoden vergleichen
Prüfen Sie die ausgewählten Methoden nebeneinander; abweichende Zeilen sind hervorgehoben.
| Analyse kausaler Effekte bei heterogener Behandlung× | Analyse von unterbrochenen Zeitreihen (Interrupted Time Series, ITS)× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2015-2016 | 2002 |
| Urheber≠ | Brodersen et al. (causal impact framework, 2015); Athey & Imbens (HTE estimation, 2016) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Typ≠ | Causal inference / heterogeneous effects estimation | Quasi-experimental segmented regression |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen≠ | HTE-CausalImpact, CATE causal impact, heterogeneous causal impact, subgroup causal impact analysis | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Heterogeneous treatment effect causal impact analysis extends the Bayesian structural time-series causal impact framework to estimate not just the average effect of an intervention but how that effect varies across subgroups or individual units. By combining counterfactual prediction with conditional average treatment effect (CATE) estimation, it reveals which groups benefit most or least from an intervention. | 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. |
| ScholarGateDatensatz ↗ |
|
|