方法对比

	因果关系的敏感性分析	因果推断的工具变量（IV）方法
领域	因果推断	卫生经济学
方法族	Regression model	Process / pipeline
起源年份	1983–2002	1990s (modern applications)
提出者	Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)	Angrist & Pischke (applied econometrics); rooted in econometric theory
类型	Diagnostic / robustness check	Method
开创性文献	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.
别名	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity	IV, two-stage least squares, TSLS, causal estimation
相关	4	3
摘要	Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis.	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.