Salīdzināt metodes

	Kontrafaktiskās ietekmes novērtēšana (CIE)	Sintētiskās kontroles metode (SCM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1970s–2000s	2003–2010
Autors	Heckman, Imbens, Rubin, and the program evaluation literature	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tips	Causal inference / program evaluation	Quasi-experimental causal inference
Pirmavots	Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.	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	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Saistītās	5	4
Kopsavilkums	Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.