方法对比

	贝叶斯敏感性分析用于因果关系	Marginal Structural Model (MSM)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2000s–2010s	2000
提出者	McCandless, Gustafson & Austin (2007); Gustafson (2015)	James M. Robins, Miguel A. Hernan, Babette Brumback
类型	Bayesian causal sensitivity analysis	Causal model / semiparametric weighting
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.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
别名	Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
相关	6	5
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.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.