Σύγκριση μεθόδων

	Ανάλυση ευαισθησίας για την αιτιότητα στην έρευνα εκπαίδευσης	Μέθοδος Εργαλειακών Μεταβλητών (IV) για Αιτιώδη Συμπερασματολογία
Πεδίο	Αιτιακή Συμπερασματολογία	Οικονομικά της Υγείας
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	1983–2002	1990s (modern applications)
Δημιουργός	Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others	Angrist & Pischke (applied econometrics); rooted in econometric theory
Τύπος	Causal robustness / bias assessment	Method
Θεμελιώδης πηγή	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Εναλλακτικές ονομασίες	Rosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studies	IV, two-stage least squares, TSLS, causal estimation
Συναφείς	6	3
Σύνοψη	Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings.	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.