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| Robuste Methode der synthetischen Kontrolle× | Sensitivitätsanalyse für Kausalität× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2021 | 1983–2002 |
| Urheber≠ | Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010) | Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach) |
| Typ≠ | Quasi-experimental causal inference | Diagnostic / robustness check |
| Wegweisende Quelle≠ | 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 ↗ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| Aliasnamen | Robust SCM, Inference-robust synthetic control, Synthetic control with valid inference, SCM with prediction intervals | sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | 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. | Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis. |
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