Salīdzināt metodes

	Bejonsas sintētiskās kontroles metode	Diferenču starpībām (Diff-in-Diff)
Nozare	Cēloņsakarību secināšana	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)	1994
Autors	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tips	Bayesian causal inference / synthetic control	Causal inference / panel regression
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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Citi nosaukumi	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.