Comparer des méthodes

	Évaluation d'Impact Contrefactuel Spatial (EICS)	Différence-en-différences (Diff-in-Diff)
Domaine	Inférence causale	Économétrie
Famille	Regression model	Regression model
Année d'origine	2010s	1994
Auteur d'origine	Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Quasi-experimental / causal inference	Causal inference / panel regression
Source fondatrice	Cerqua, A., & Pellegrini, G. (2014). Do subsidies to private capital boost firms' growth? A multiple regression discontinuity design approach. Journal of Public Economics, 109, 114-126.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	SCIE, spatial CIE, place-based counterfactual evaluation, regional counterfactual analysis	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Apparentées	5	5
Résumé	Spatial Counterfactual Impact Evaluation (SCIE) is a family of quasi-experimental methods that estimate the causal effect of geographically targeted policies — such as EU Cohesion Funds, enterprise zones, or place-based subsidies — by constructing a spatial counterfactual: what outcomes the treated region would have experienced without the intervention, inferred from comparable untreated regions or from discontinuities at policy boundaries.	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.