Regression modelQuasi-experimental / causal inference
异质性处理效应逆概率加权法 (HTE-IPW)
HTE-IPW 将标准的逆概率加权法扩展，以恢复因果效应在亚组或协变量值之间如何变化。通过将每个观测值按其估计的处理概率的倒数进行重加权，该方法创建了一个伪总体，在该总体中处理与背景特征无关，然后估计条件平均处理效应 (CATE) 作为这些特征的函数。
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来源
- Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189. DOI: 10.1111/1468-0262.00442 ↗
- Abrevaya, J., Hsu, Y.-C., & Lieli, R. P. (2015). Estimating conditional average treatment effects. Journal of Business and Economic Statistics, 33(4), 485-505. DOI: 10.1080/07350015.2014.975555 ↗
如何引用本页
ScholarGate. (2026, June 3). Heterogeneous Treatment Effect Estimation via Inverse Probability Weighting. ScholarGate. https://scholargate.app/zh/causal-inference/heterogeneous-treatment-effect-inverse-probability-weighting
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- 双重稳健估计（AIPW）因果推断↔ 比较
- 异质性处理效应倾向得分匹配因果推断↔ 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
- 倾向得分加权法 (PSW / IPW)因果推断↔ 比较
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