Linganisha mbinu

	Jaribio la Bayesian la Placebo	Njia ya Bayesian Synthetic Control
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2010-2015	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)
Mwanzilishi	Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition)	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller
Aina	Robustness check / falsification test	Bayesian causal inference / synthetic control
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.	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	Bayesian falsification test, Bayesian permutation placebo, Bayesian robustness check, Bayesian in-time placebo	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC
Zinazohusiana	5	5
Muhtasari	The Bayesian Placebo Test is a falsification strategy for causal inference that applies Bayesian inference to placebo scenarios — either fake treatments in the pre-intervention period, on unaffected units, or at fictitious cut-offs — to verify that observed treatment effects cannot plausibly arise by chance or from a misspecified model. It integrates prior information and yields posterior distributions of placebo effects for direct probabilistic comparison.	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.