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| 贝叶斯倾向得分匹配× | 熵平衡× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份 | 2012 | 2012 |
| 提出者≠ | Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983) | Jens Hainmueller |
| 类型≠ | Bayesian causal inference / matching | Covariate-balancing reweighting |
| 开创性文献≠ | Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609. 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 | Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting | EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing |
| 相关 | 6 | 6 |
| 摘要≠ | Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection. | 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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