Regression modelQuasi-experimental / causal inference
空间因果敏感性分析
空间因果敏感性分析系统地检验从地理参考数据得出的因果估计，在空间结构、溢出效应和空间权重矩阵选择发生变化时是否仍然成立。由于邻近单元通常共享未测量的混淆因素（如土壤质量、当地基础设施、社区规范），因此简单的回归可能会产生有偏的因果估计。该方法揭示了声称的因果效应在多大程度上对替代的空间设定是脆弱或稳健的。
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来源
- Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322
- Reich, B. J., Yang, S., Guan, Y., Giffin, A. B., Miller, M. J., & Rappold, A. G. (2021). A review of spatial causal inference methods for environmental and epidemiological applications. International Statistical Review, 89(3), 605-634. DOI: 10.1111/insr.12452 ↗
如何引用本页
ScholarGate. (2026, June 3). Spatial Sensitivity Analysis for Causal Inference. ScholarGate. https://scholargate.app/zh/causal-inference/spatial-sensitivity-analysis-for-causality
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.
- 双重差分法 (Diff-in-Diff)计量经济学↔ compare
- 地理加权回归 (GWR)空间分析↔ compare
- 因果推断的工具变量（IV）方法卫生经济学↔ compare
- 倾向得分匹配研究统计学↔ compare
- 空间误差模型 (SEM)空间分析↔ compare
- 空间滞后模型（SAR / 空间自回归）空间分析↔ compare