方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 贝叶斯倾向得分匹配× | 粗化精确匹配 (CEM)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2012 | 2011-2012 |
| 提出者≠ | Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983) | Iacus, King, & Porro |
| 类型≠ | Bayesian causal inference / matching | Matching / causal inference |
| 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 ↗ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ |
| 别名≠ | Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting | CEM, coarsened matching, monotonic imbalance bounding matching |
| 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. | Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model. |
| ScholarGate数据集 ↗ |
|
|