Regression modelQuasi-experimental / causal inference
稳健逆概率加权法 (Robust IPW)
稳健逆概率加权法是一种因果推断估计量，它通过稳定化或截尾的倾向得分权重对观测单元进行重加权，然后应用sandwich或bootstrap方差估计来防范模型失拟、极端权重和标准误差膨胀。它扩展了标准的IPW，以提高观察性研究中的有限样本表现和推断可靠性。
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来源
- Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960. DOI: 10.1002/sim.1903 ↗
- Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI: 10.1097/00001648-200009000-00011 ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Inverse Probability Weighting Estimator. ScholarGate. https://scholargate.app/zh/causal-inference/robust-inverse-probability-weighting
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- 双重稳健估计（AIPW）因果推断↔ 比较
- 逆概率治疗加权法 (IPW / IPTW)因果推断↔ 比较
- Marginal Structural Model (MSM)因果推断↔ 比较
- 倾向得分匹配研究统计学↔ 比较
- 倾向得分加权法 (PSW / IPW)因果推断↔ 比较
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