Módszerek összehasonlítása

	Robuszt instrumentális változós becslés	Instrumentális Változók (IV) Módszer Kauzális Infláció Becslésére
Tudományterület	Oksági következtetés	Egészség-gazdaságtan
Módszercsalád	Regression model	Process / pipeline
Keletkezés éve	1949–2019	1990s (modern applications)
Megalkotó	Anderson & Rubin (1949); Stock, Wright & Yogo (2002); Andrews, Stock & Sun (2019)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Típus	Causal inference / robust estimation	Method
Alapmű	Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518-529.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alternatív nevek	Robust IV, Weak-instrument-robust IV, Robust 2SLS, Weak-instrument-robust inference	IV, two-stage least squares, TSLS, causal estimation
Kapcsolódó	4	3
Összefoglaló	Robust Instrumental Variables estimation extends standard IV and two-stage least squares (2SLS) by guarding against weak-instrument bias and non-standard inference. Methods such as the Anderson-Rubin test, Limited Information Maximum Likelihood (LIML), and the Conditional Likelihood Ratio test provide valid confidence sets and hypothesis tests even when instruments are weak or only partially identified, making IV inference reliable in settings where standard 2SLS breaks down.	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.