Comparer des méthodes

	Méthode de contrôle synthétique pour données de panel	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010	1973
Auteur d'origine	Alberto Abadie, Alexis Diamond & Jens Hainmueller	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Causal inference / panel data	Nonparametric matching / causal inference
Source fondatrice	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., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	SCM panel, panel synthetic control, synthetic control estimator, comparative case study	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	5	6
Résumé	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.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.