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。
| 多期粗糙化精确匹配× | 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≠ | 2012–2021 | 2011-2012 |
| 提出者≠ | Iacus, King & Porro (CEM, 2012); extended to multi-period panel settings | Iacus, King, & Porro |
| 类型≠ | Non-parametric matching / causal inference | Matching / causal inference |
| 开创性文献 | Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24. 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≠ | Multi-period CEM, Longitudinal CEM, Panel CEM, Multi-wave CEM | CEM, coarsened matching, monotonic imbalance bounding matching |
| 相关 | 6 | 6 |
| 摘要≠ | Multi-period Coarsened Exact Matching (multi-period CEM) extends the CEM framework of Iacus, King, and Porro to longitudinal data with multiple pre- and post-treatment periods. It bins continuous covariates into coarsened categories, matches treated and control units that fall into the same cells across all relevant time periods, and then estimates a weighted average treatment effect that accounts for temporal structure. | 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 ↗ |
|
|