Regression modelQuasi-experimental / causal inference
贝叶斯双重稳健估计
贝叶斯双重稳健估计将经典的双重稳健（DR）增强型逆概率加权框架与贝叶斯推断相结合。它同时对倾向得分和结果回归进行建模，对两者都赋予先验分布，并推导出平均处理效应的后验分布，即使两个分量模型中的一个被错误指定，该估计量仍然是一致的。
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来源
- Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973. DOI: 10.1111/j.1541-0420.2005.00377.x ↗
- Scharfstein, D., Nabi, R., Kennedy, E. H., Huang, M.-Y., Bonvini, M., & Smid, M. (2021). Semiparametric sensitivity analysis: Unmeasured confounding in observational studies. arXiv:1910.14694. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Doubly Robust Estimation of Average Treatment Effects. ScholarGate. https://scholargate.app/zh/causal-inference/bayesian-doubly-robust-estimation
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.
- 贝叶斯因果效应分析因果推断↔ compare
- 贝叶斯倾向得分匹配因果推断↔ compare
- 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
- Marginal Structural Model (MSM)因果推断↔ compare