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| 패널 데이터 인과적 영향 분석× | 패널 데이터 이중차분법 (패널 DiD / TWFE)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2015 (base method); panel extension mid-2010s | 1985–2004 |
| 창시자≠ | Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature | Ashenfelter & Card (1985); codified by Angrist & Pischke (2009); serial correlation critique by Bertrand, Duflo & Mullainathan (2004) |
| 유형≠ | Bayesian structural time-series causal inference | Causal inference / panel regression |
| 원전≠ | 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 ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 별칭 | Panel CausalImpact, multi-unit causal impact, panel BSTS causal inference, panel structural time-series causal analysis | Two-Way Fixed Effects DiD, TWFE, Panel DiD, Panel Diff-in-Diff |
| 관련≠ | 6 | 4 |
| 요약≠ | 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 Difference-in-Differences extends the classic two-period DiD design to settings with multiple units observed across many time periods. By absorbing unit-level fixed effects and time fixed effects simultaneously, it isolates the causal effect of a treatment or policy change while controlling for both time-invariant unit heterogeneity and common time shocks affecting all units. |
| ScholarGate데이터셋 ↗ |
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