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。
| CEM在教育研究中的应用× | 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 | Iacus, King, & Porro | Iacus, King, & Porro |
| 类型≠ | Matching / quasi-experimental | 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≠ | CEM in education, CEM for educational studies, exact matching education, coarsened matching educational data | CEM, coarsened matching, monotonic imbalance bounding matching |
| 相关≠ | 4 | 6 |
| 摘要≠ | Coarsened Exact Matching (CEM) is a pre-processing matching strategy that reduces imbalance between treated and comparison groups before outcome analysis. In education research it is used to create balanced comparison groups from administrative records, survey data, or quasi-experimental study designs — for example comparing students who received an intervention against comparable students who did not, without relying on randomisation. | 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 ↗ |
|
|