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| Syntetisk Kontrol Metode (SCM)× | Regression Discontinuity Design (RDD)× | |
|---|---|---|
| Fagområde | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2010 | 2008 |
| Ophavsperson≠ | Abadie, Diamond & Hainmueller | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| Type≠ | Counterfactual causal-inference model | Quasi-experimental causal design |
| Oprindelig kilde≠ | Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| Aliasser≠ | synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM) | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| Relaterede | 5 | 5 |
| Resumé≠ | The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists. | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. |
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