方法对比
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| 匹配方法（CEM / 最优 / 遗传）× | 局部平均处理效应（LATE / CACE）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2012 | 1994 |
| 提出者≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996) |
| 类型≠ | Matching for causal inference | Instrumental-variable causal estimand |
| 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 ↗ | Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475. DOI ↗ |
| 别名≠ | coarsened exact matching, optimal matching, genetic matching, CEM | LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE) |
| 相关 | 5 | 5 |
| 摘要≠ | Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching. | The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis. |
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