方法对比
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| 面板数据逆概率加权× | 倾向得分加权法 (PSW / IPW)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2000 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| 提出者≠ | Robins, Hernan & Brumback | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| 类型≠ | Reweighting / causal inference | Causal inference / reweighting |
| 开创性文献≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | 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 ↗ |
| 别名 | panel IPW, longitudinal IPW, time-varying IPW, panel IPTW | PSW, inverse probability weighting, IPW, propensity-based weighting |
| 相关≠ | 5 | 6 |
| 摘要≠ | Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods. | 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). |
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