Порівняння методів

	Оцінювання за допомогою робастних інструментальних змінних	Метод інструментальних змінних (ІЗ) для причинно-наслідкового висновку
Галузь	Причинно-наслідковий висновок	Економіка охорони здоров'я
Родина	Regression model	Process / pipeline
Рік появи	1949–2019	1990s (modern applications)
Автор методу	Anderson & Rubin (1949); Stock, Wright & Yogo (2002); Andrews, Stock & Sun (2019)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Тип	Causal inference / robust estimation	Method
Основоположне джерело	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.
Інші назви	Robust IV, Weak-instrument-robust IV, Robust 2SLS, Weak-instrument-robust inference	IV, two-stage least squares, TSLS, causal estimation
Пов'язані	4	3
Підсумок	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.