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| 강건성 합성 통제 방법× | 강건 차분-차분법× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2021 | 2021-2023 |
| 창시자≠ | Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010) | Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023) |
| 유형≠ | Quasi-experimental causal inference | Causal inference / panel regression |
| 원전≠ | Cattaneo, M. D., Feng, Y., & Titiunik, R. (2021). Prediction Intervals for Synthetic Control Methods. Journal of the American Statistical Association, 116(536), 1865-1880. 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 SCM, Inference-robust synthetic control, Synthetic control with valid inference, SCM with prediction intervals | robust DiD, heterogeneity-robust DiD, staggered DiD, disaggregated ATT DiD |
| 관련 | 5 | 5 |
| 요약≠ | The robust synthetic control method extends the classic synthetic control estimator by providing statistically valid uncertainty quantification and inference. Developed by Cattaneo, Feng and Titiunik (2021), it addresses a core limitation of the original approach — the lack of formal prediction intervals — making causal conclusions more defensible when only a single treated unit is observed. | 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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