Módszerek összehasonlítása

	Térbeli Fuzzy Megszakadási Regressziós Terv	Instrumentális Változók (IV) Módszer Kauzális Infláció Becslésére
Tudományterület	Oksági következtetés	Egészség-gazdaságtan
Módszercsalád	Regression model	Process / pipeline
Keletkezés éve	2015	1990s (modern applications)
Megalkotó	Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Típus	Quasi-experimental causal inference / IV-based spatial design	Method
Alapmű	Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alternatív nevek	Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD	IV, two-stage least squares, TSLS, causal estimation
Kapcsolódó	5	3
Összefoglaló	Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic RDD with the instrumental-variable correction for imperfect compliance used in fuzzy RDD.	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.