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| 面板数据倾向得分加权× | 倾向得分匹配× | |
|---|---|---|
| 领域≠ | 因果推断 | 研究统计学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2000-2003 | 1983 |
| 提出者≠ | Hirano, Imbens & Ridder; Robins, Hernan & Brumback | Paul Rosenbaum and Donald Rubin |
| 类型≠ | Causal inference / panel weighting | Method |
| 开创性文献≠ | 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 ↗ | 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 PSW, panel IPW, longitudinal propensity score weighting, panel inverse probability weighting | PSM, propensity score weighting, covariate balance |
| 相关≠ | 5 | 3 |
| 摘要≠ | Panel Data Propensity Score Weighting (panel PSW) extends inverse probability weighting to longitudinal settings where the same units are observed across multiple time periods. It reweights observations by the inverse of each unit's time-varying probability of receiving treatment, creating a pseudo-population in which treatment is balanced on observed covariates at each period, and then estimates causal effects from repeated-measures data. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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