Compară metode

	Test placebo pentru evaluarea politicilor	Metoda Controlului Sintetic (MCS)
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	1990s–2000s	2003–2010
Autorul original	Bertrand, Duflo & Mullainathan (2004 canonical formalization); Imbens & Wooldridge (2009 textbook treatment)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tip	Falsification / specification check	Quasi-experimental causal inference
Sursa seminală	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.	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.
Denumiri alternative	placebo test, falsification test, fake treatment test, placebo regression	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Înrudite	4	4
Rezumat	A policy evaluation placebo test is a falsification check used in quasi-experimental research to validate a causal identification strategy. The researcher applies the same estimation method to a pseudo-treatment — a time period, group, or outcome where the real policy could not have had an effect — and checks that no spurious effect is detected. A null placebo result builds confidence that the main estimate reflects a genuine causal impact rather than bias or confounding.	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.