Regression modelQuasi-experimental / causal inference
机器学习增强边际结构模型 (ML-MSM)
机器学习增强边际结构模型结合了 Robins 等人 MSM 框架的因果严谨性与灵活、数据自适应的机器学习算法，用于估计倾向得分和结果模型。通过用集成学习器或神经网络替换参数化模型，ML-MSM 在不依赖于正确指定的参数形式的情况下，可以恢复无混杂的有效因果估计。
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Method map
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来源
- 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 ↗
- Luedtke, A. R., & van der Laan, M. J. (2016). Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy. Annals of Statistics, 44(2), 713-742. DOI: 10.1214/15-AOS1384 ↗
如何引用本页
ScholarGate. (2026, June 3). Machine Learning-Augmented Marginal Structural Model with Flexible Nuisance Estimation. ScholarGate. https://scholargate.app/zh/causal-inference/machine-learning-augmented-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.
- 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
- 机器学习增强双重稳健估计 (ML-DR)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