方法对比
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| 面板数据粗化精确匹配× | 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 (CEM); 2021 (panel extension) | 2011-2012 |
| 提出者≠ | Iacus, King & Porro (CEM, 2012); panel extension via Imai, Kim & Wang (2021) | 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 | Panel CEM, CEM for panel data, coarsened exact matching with panel data | CEM, coarsened matching, monotonic imbalance bounding matching |
| 相关 | 6 | 6 |
| 摘要≠ | Panel Data Coarsened Exact Matching applies the Coarsened Exact Matching (CEM) algorithm to repeated-measures panel data, matching treated and control units within the same coarsened covariate strata across multiple time periods. It balances pre-treatment characteristics before estimating a causal treatment effect, combining the transparency of exact matching with the richer identification available in longitudinal datasets. | 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. |
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