Regression modelQuasi-experimental / causal inference
因果关系的敏感性分析
因果关系的敏感性分析评估因果结论对未观测混淆因素的稳健性。它不假设所有混淆因素都得到了控制，而是询问：一个未测量的变量需要多强才能推翻估计的效果？它是任何准实验或观察性因果分析后不可或缺的稳健性检查。
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Method map
The neighbourhood of related methods — select a node to explore.
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来源
- Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
- Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. Journal of the Royal Statistical Society: Series B, 82(1), 39-67. DOI: 10.1111/rssb.12348 ↗
如何引用本页
ScholarGate. (2026, June 3). Sensitivity Analysis for Hidden Bias in Causal Inference. ScholarGate. https://scholargate.app/zh/causal-inference/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
- 双重稳健估计（AIPW）因果推断↔ 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