方法对比

	动态逆概率加权	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	1986-2000	2000
提出者	James M. Robins and colleagues	James M. Robins, Miguel A. Hernan, Babette Brumback
类型	Causal weighting estimator	Causal model / semiparametric weighting
开创性文献	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
别名	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
相关	4	5
摘要	Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.	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.