Salīdzināt metodes

	Bejonsas sintētiskās kontroles metode	Panel Data Synthetic Control Method
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)	2010
Autors	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller	Alberto Abadie, Alexis Diamond & Jens Hainmueller
Tips	Bayesian causal inference / synthetic control	Causal inference / panel data
Pirmavots	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.
Citi nosaukumi	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC	SCM panel, panel synthetic control, synthetic control estimator, comparative case study
Saistītās	5	5
Kopsavilkums	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.