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| 倾向得分加权法 (PSW / IPW)× | 熵平衡× | |
|---|---|---|
| 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) | 2012 |
| 提出者≠ | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) | Jens Hainmueller |
| 类型≠ | Causal inference / reweighting | Covariate-balancing reweighting |
| 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 ↗ | Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46. DOI ↗ |
| 别名 | PSW, inverse probability weighting, IPW, propensity-based weighting | EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing |
| 相关 | 6 | 6 |
| 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). | Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step. |
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