Regression modelQuasi-experimental / causal inference
贝叶斯边际结构模型
贝叶斯边际结构模型（Bayesian MSM）结合了逆概率加权边际结构模型的因果识别能力和贝叶斯后验推断。它不依赖于点估计和渐近标准误差，而是通过因果效应参数的完整后验分布来传播不确定性，为时变处理的因果效应提供一致的不确定性量化。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288. DOI: 10.1111/biom.12269 ↗
- Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI: 10.1097/00001648-200009000-00011 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Marginal Structural Model with Inverse Probability Weighting. ScholarGate. https://scholargate.app/zh/causal-inference/bayesian-marginal-structural-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 贝叶斯双重差分法因果推断↔ compare
- 贝叶斯工具变量 (Bayesian IV)因果推断↔ compare
- 双重稳健估计（AIPW）因果推断↔ compare
- 逆概率治疗加权法 (IPW / IPTW)因果推断↔ compare
- Marginal Structural Model (MSM)因果推断↔ compare
- 倾向得分加权法 (PSW / IPW)因果推断↔ compare