Comparer des méthodes

	Évaluation d'impact contrefactuel multi-périodes	Différence-en-différences (Diff-in-Diff)
Domaine	Inférence causale	Économétrie
Famille	Regression model	Regression model
Année d'origine	2000s–2010s	1994
Auteur d'origine	Developed through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometricians	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Causal inference / quasi-experimental evaluation	Causal inference / panel regression
Source fondatrice	Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	multi-period CIE, longitudinal counterfactual evaluation, dynamic counterfactual impact evaluation, multi-wave CIE	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Apparentées	4	5
Résumé	Multi-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period evaluations, it tracks treatment effects as they evolve over time, capturing dynamic, delayed, or fading impacts that a two-period comparison would miss.	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.