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| 机器学习增强的粗糙精确匹配 (ML-CEM)× | 匹配估计量× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2012-2019 | 1973 |
| 提出者≠ | Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| 类型≠ | Matching / quasi-experimental | Nonparametric 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 ↗ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ |
| 别名 | ML-augmented CEM, ML-CEM, automated coarsened exact matching, ML-assisted CEM | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| 相关 | 6 | 6 |
| 摘要≠ | Machine Learning-Augmented Coarsened Exact Matching extends Coarsened Exact Matching (Iacus, King & Porro, 2012) by using supervised machine learning to automate and optimise the coarsening step — the discretisation of continuous covariates into bins — rather than relying on researcher-specified cutpoints. This reduces both ad hoc subjectivity in coarsening decisions and residual imbalance, while preserving CEM's core logic of exact matching within coarsened strata. | The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome. |
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