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| Synthetische Kontrollmethode (SCM)× | Matching Methods (CEM / Optimal / Genetic)× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2010 | 2012 |
| Urheber≠ | Abadie, Diamond & Hainmueller | Iacus, King & Porro (CEM); Hansen (optimal/full matching) |
| Typ≠ | Counterfactual causal-inference model | Matching for causal inference |
| Wegweisende Quelle≠ | 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 ↗ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ |
| Aliasnamen≠ | synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM) | coarsened exact matching, optimal matching, genetic matching, CEM |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching. |
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