Comparar métodos

	Método Sintético de Control Robusto	Método de Control Sintético (SCM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2021	2003–2010
Autor original	Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipo	Quasi-experimental causal inference	Quasi-experimental causal inference
Fuente seminal	Cattaneo, M. D., Feng, Y., & Titiunik, R. (2021). Prediction Intervals for Synthetic Control Methods. Journal of the American Statistical Association, 116(536), 1865-1880.	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.
Alias	Robust SCM, Inference-robust synthetic control, Synthetic control with valid inference, SCM with prediction intervals	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionados	5	4
Resumen	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.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.