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| 강건 시계열 중단 분석× | 강건 차분-차분법× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2010s | 2021-2023 |
| 창시자≠ | Bernal, Cummins & Gasparrini; Linden (robust extensions) | Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023) |
| 유형≠ | Quasi-experimental segmented regression with robust inference | Causal inference / panel regression |
| 원전≠ | 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 ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| 별칭 | robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA | robust DiD, heterogeneity-robust DiD, staggered DiD, disaggregated ATT DiD |
| 관련 | 5 | 5 |
| 요약≠ | Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research and public-health evaluation when the time series contains influential observations, non-constant variance, or mild autocorrelation. | Robust Difference-in-Differences is a family of modern DiD estimators designed to remain valid when treatment timing is staggered across units and treatment effects are heterogeneous over time or across groups. Classical two-way fixed-effects (TWFE) DiD can be severely biased in such settings; robust variants estimate group-time average treatment effects (ATTs) separately and then aggregate them in a theoretically sound way. |
| ScholarGate데이터셋 ↗ |
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