Sammenlign metoder

	Fuzzy Regression Discontinuity for Policy Evaluation	Instrumentalvariabel (IV) Metoden til Kausal Inferens
Fagområde	Kausal inferens	Sundhedsøkonomi
Familie	Regression model	Process / pipeline
Oprindelsesår	2001	1990s (modern applications)
Ophavsperson	Hahn, Todd & Van der Klaauw	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Quasi-experimental / local IV estimator	Method
Oprindelig kilde	Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Review of Economic Studies, 68(1), 201-209.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Aliasser	Fuzzy RDD, Fuzzy RD, Fuzzy Regression Discontinuity, Imperfect Compliance RDD	IV, two-stage least squares, TSLS, causal estimation
Relaterede	5	3
Resumé	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates the causal effect of a policy when eligibility is determined by crossing a threshold on a continuous score, but actual take-up or compliance is imperfect. Developed formally by Hahn, Todd, and Van der Klaauw (2001), it uses the threshold as an instrumental variable to recover a Local Average Treatment Effect (LATE) among compliers near the cutoff.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.