Comparar métodos

	Método de Control Sintético de Efecto de Tratamiento Heterogéneo	Método de Control Sintético para Datos de Panel
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2010-2021	2010
Autor original	Abadie, Diamond & Hainmueller (SCM foundation); Ben-Michael, Feller & Rothstein (augmented/HTE extensions)	Alberto Abadie, Alexis Diamond & Jens Hainmueller
Tipo	Quasi-experimental causal inference	Causal inference / panel data
Fuente 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.	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	HTE-SCM, heterogeneous SCM, heterogeneous synthetic control, SCM with HTE	SCM panel, panel synthetic control, synthetic control estimator, comparative case study
Relacionados	6	5
Resumen	The Heterogeneous Treatment Effect Synthetic Control Method (HTE-SCM) extends the classical synthetic control framework by allowing the causal effect of an intervention to vary across time periods, subgroups, or outcome dimensions rather than collapsing it to a single average estimate. It combines the counterfactual donor-pool matching logic of Abadie et al. (2010) with modern heterogeneous-effects machinery to recover time-varying or subgroup-specific treatment paths.	The panel data synthetic control method estimates the causal effect of an intervention on a single treated unit by constructing a data-driven weighted combination of untreated units — a synthetic control — that best reproduces the treated unit's pre-treatment outcome trajectory. The post-treatment gap between the treated unit and its synthetic counterpart is the estimated treatment effect.