Σύγκριση μεθόδων

	Ανάλυση Ευαισθησίας Ενισχυμένη με Μηχανική Μάθηση για την Αιτιότητα	Μέθοδος Εργαλειακών Μεταβλητών (IV) για Αιτιώδη Συμπερασματολογία
Πεδίο	Αιτιακή Συμπερασματολογία	Οικονομικά της Υγείας
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	2018-2020	1990s (modern applications)
Δημιουργός	Cinelli & Hazlett (sensitivity framework); Chernozhukov et al. (ML augmentation for causal estimation)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Τύπος	Sensitivity analysis / causal robustness assessment	Method
Θεμελιώδης πηγή	Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Εναλλακτικές ονομασίες	ML-augmented sensitivity analysis, ML sensitivity analysis for causality, machine learning sensitivity analysis, debiased ML sensitivity analysis	IV, two-stage least squares, TSLS, causal estimation
Συναφείς	5	3
Σύνοψη	Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity tools, it delivers both high-dimensional covariate adjustment and transparent communication of remaining uncertainty about unobserved confounders.	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.