Comparer des méthodes

	Méthode de Contrôle Synthétique Dynamique	Différence-en-différences (Diff-in-Diff)
Domaine	Inférence causale	Économétrie
Famille	Regression model	Regression model
Année d'origine	2010	1994
Auteur d'origine	Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Comparative case study / counterfactual estimation	Causal inference / panel regression
Source fondatrice	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	Dynamic SCM, Time-varying synthetic control, Multi-period synthetic control, DSC	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Apparentées	5	5
Résumé	The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically.	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.