Linganisha mbinu

	Mbinu Imara ya Udhibiti wa Kisintetiki	Njia ya Bayesian Synthetic Control
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2021	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)
Mwanzilishi	Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010)	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller
Aina	Quasi-experimental causal inference	Bayesian causal inference / synthetic control
Chanzo asilia	Cattaneo, M. D., Feng, Y., & Titiunik, R. (2021). Prediction Intervals for Synthetic Control Methods. Journal of the American Statistical Association, 116(536), 1865-1880.	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.
Majina mbadala	Robust SCM, Inference-robust synthetic control, Synthetic control with valid inference, SCM with prediction intervals	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC
Zinazohusiana	5	5
Muhtasari	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 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.