Salīdzināt metodes

	Dizains politikas novērtēšanai notikumu pētījuma ietvaros	Metode sintētiskās kontroles (SCM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1993-2021	2010
Autors	Andrews (1993), MacKinlay (1997); formalized for policy evaluation by Freyaldenhoven, Hansen & Shapiro (2019) and Callaway & Sant'Anna (2021)	Abadie, Diamond & Hainmueller
Tips	Quasi-experimental / causal inference	Counterfactual causal-inference model
Pirmavots	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.	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	event study, event-study DiD, dynamic DiD, PEESD	synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)
Saistītās	5	5
Kopsavilkums	A policy evaluation event study design is a quasi-experimental approach that estimates causal effects of a policy by plotting treatment-period-by-period coefficients around a common event time. It extends difference-in-differences to visualize both pre-treatment parallel trends and the dynamic post-treatment evolution of the policy effect, and has become the standard credibility check in applied policy research.	The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.