Regression modelQuasi-experimental / causal inference
空间边际结构模型
空间边际结构模型（Spatial MSM）将经典的边际结构模型扩展到单元地理分布且空间依赖性（如邻里溢出、聚类和空间混杂）可能导致因果估计偏差的场景。它通过构建考虑了个体协变量和空间位置的逆概率权重来估计空间变化暴露的因果效应，然后在所得的伪总体中拟合加权结果模型。
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Method map
The neighbourhood of related methods — select a node to explore.
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 ↗
- Schnell, P. M., & Papadogeorgou, G. (2020). Mitigating unobserved spatial confounding when estimating the effect of supermarket access on cardiovascular disease deaths. Annals of Applied Statistics, 14(2), 793-816. DOI: 10.1214/20-aoas1377 ↗
如何引用本页
ScholarGate. (2026, June 3). Spatial Marginal Structural Model with Inverse Probability Weighting. ScholarGate. https://scholargate.app/zh/causal-inference/spatial-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.
- 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
- 空间双重稳健估计因果推断↔ compare
- 空间工具变量（Spatial IV / Spatial 2SLS）因果推断↔ compare
- 空间倾向得分匹配因果推断↔ compare