Linganisha mbinu

	Njia ya Bayesian Synthetic Control	Njia ya Kidhibiti Sanisi cha Data ya Paneli
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)	2010
Mwanzilishi	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller	Alberto Abadie, Alexis Diamond & Jens Hainmueller
Aina	Bayesian causal inference / synthetic control	Causal inference / panel data
Chanzo asilia	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	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.
Majina mbadala	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC	SCM panel, panel synthetic control, synthetic control estimator, comparative case study
Zinazohusiana	5	5
Muhtasari	The Bayesian Synthetic Control Method estimates the causal effect of an intervention on a single treated unit by constructing a probabilistic counterfactual from a weighted combination of untreated donor units. Unlike the classical SCM, it places a prior distribution over the synthetic weights, yielding full posterior uncertainty intervals for the counterfactual trajectory and the treatment effect at each post-intervention time point.	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.