Comparer des méthodes

	Test de placebo pour l'évaluation de politiques	Méthode du Contrôle Synthétique (MCS)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	2003–2010
Auteur d'origine	Bertrand, Duflo & Mullainathan (2004 canonical formalization); Imbens & Wooldridge (2009 textbook treatment)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Type	Falsification / specification check	Quasi-experimental causal inference
Source fondatrice	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.
Alias	placebo test, falsification test, fake treatment test, placebo regression	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Apparentées	4	4
Résumé	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.