Compara mètodes

	Mètode del Control Sintètic (SCM)	Disseny de Regressió per Discontinuïtat (RDD)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2010	2008
Autor original	Abadie, Diamond & Hainmueller	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Tipus	Counterfactual causal-inference model	Quasi-experimental causal design
Font seminal	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.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Àlies	synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Relacionats	5	5
Resum	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.