Regression modelQuasi-experimental / causal inference
贝叶斯敏感性分析用于因果关系
贝叶斯敏感性分析用于因果关系，量化了一个未测量混淆因素需要多大程度地同时影响处理分配和结果才能推翻因果结论。它不测试单一的最坏情况，而是对隐藏混淆的强度设置先验分布，通过完整的贝叶斯模型传播不确定性，并报告因果效应的后验分布，从而诚实地反映从观测数据中可以识别和不能识别的内容。
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来源
- McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718. DOI: 10.1002/sim.3460 ↗
- Gustafson, P. (2015). Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data. CRC Press / Chapman & Hall. ISBN: 9781439869390
如何引用本页
ScholarGate. (2026, June 3). Bayesian Sensitivity Analysis for Unmeasured Confounding in Causal Inference. ScholarGate. https://scholargate.app/zh/causal-inference/bayesian-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.
- 贝叶斯双重差分法因果推断↔ 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
- Marginal Structural Model (MSM)因果推断↔ compare
- 倾向得分匹配研究统计学↔ compare
- 因果关系的敏感性分析因果推断↔ compare