方法对比
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| 倾向得分加权法 (PSW / IPW)× | 逆概率治疗加权法 (IPW / IPTW)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1983 (propensity score); 2003 (efficient IPW estimator) | 2000 |
| 提出者≠ | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) | Robins, Hernán & Brumback |
| 类型≠ | Causal inference / reweighting | Causal inference weighting estimator |
| 开创性文献≠ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 别名≠ | PSW, inverse probability weighting, IPW, propensity-based weighting | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 相关≠ | 6 | 5 |
| 摘要≠ | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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