Linganisha mbinu

	Njia ya Kidhibiti Sanisi cha Data ya Paneli	Kikokotozi cha Kulinganisha
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2010	1973
Mwanzilishi	Alberto Abadie, Alexis Diamond & Jens Hainmueller	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Aina	Causal inference / panel data	Nonparametric matching / causal inference
Chanzo asilia	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.
Majina mbadala	SCM panel, panel synthetic control, synthetic control estimator, comparative case study	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Zinazohusiana	5	6
Muhtasari	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.