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	Robust Fuzzy Regression Discontinuity Design	Differens-i-differens (DiD)
Ämnesområde	Kausal inferens	Ekonometri
Familj	Regression model	Regression model
Ursprungsår	2014 (robust CCT estimator); 2001 (fuzzy RDD formalization)	1994
Upphovsperson	Calonico, Cattaneo, and Titiunik (robust inference framework); Hahn, Todd, and Van der Klaauw (fuzzy RDD formalization)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Typ	Quasi-experimental causal inference with IV at threshold	Causal inference / panel regression
Ursprungskälla	Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	Robust Fuzzy RDD, Fuzzy RD with robust inference, bias-corrected fuzzy RD, CCT fuzzy RDD	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Närliggande	5	5
Sammanfattning	Robust Fuzzy Regression Discontinuity Design estimates a local average treatment effect (LATE) at a threshold where crossing the cutoff raises — but does not guarantee — treatment receipt. Introduced by Calonico, Cattaneo, and Titiunik (2014), the robust framework applies bias-corrected local polynomial estimation with a robust variance estimator, correcting the coverage failures of conventional bandwidth-optimal inference in both the sharp and fuzzy cases.	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.