Comparer des méthodes

	Évaluation de politiques par devis-régression (Regression Discontinuity Design)	Différence-en-différences (Diff-in-Diff)
Domaine	Inférence causale	Économétrie
Famille	Regression model	Regression model
Année d'origine	1960; policy evaluation applications widespread from 2000s	1994
Auteur d'origine	Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Quasi-experimental causal design	Causal inference / panel regression
Source fondatrice	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Apparentées	5	5
Résumé	Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves.	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.