方法对比

	贝叶斯敏感性分析用于因果关系	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	2000s–2010s	1990s (modern applications)
提出者	McCandless, Gustafson & Austin (2007); Gustafson (2015)	Angrist & Pischke (applied econometrics); rooted in econometric theory
类型	Bayesian causal sensitivity analysis	Method
开创性文献	McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
别名	Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis	IV, two-stage least squares, TSLS, causal estimation
相关	6	3
摘要	Bayesian sensitivity analysis for causality quantifies how much an unmeasured confounder would need to influence both treatment assignment and outcome to overturn a causal conclusion. Rather than testing a single worst-case scenario, it places prior distributions over the strength of hidden confounding, propagates uncertainty through a full Bayesian model, and reports a posterior distribution for the causal effect that honestly reflects what is and is not identified from observed data.	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.