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异质性处理效应边际结构模型 (HTE-MSM)×逆概率治疗加权法 (IPW / IPTW)×
领域因果推断因果推断
方法族Regression modelRegression model
起源年份2000–2010s2000
提出者Robins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010sRobins, Hernán & Brumback
类型Causal inference / weighted regression with effect modificationCausal inference weighting estimator
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. DOI ↗Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗
别名HTE-MSM, heterogeneous MSM, subgroup MSM, effect-modified marginal structural modelIPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
相关55
摘要The Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations with inverse probability of treatment weights (IPTW) and interacting the treatment with effect modifiers in the weighted outcome model, the approach produces subgroup-specific or continuous causal effect estimates from observational data.Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.
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  3. PUBLISHED

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ScholarGate方法对比: Heterogeneous Treatment Effect Marginal Structural Model · Inverse Probability Weighting. 于 2026-06-19 检索自 https://scholargate.app/zh/compare