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| 贝叶斯匹配估计量× | 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≠ | 1978–1998 | 1973 |
| 提出者≠ | Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization) | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| 类型≠ | Bayesian causal inference / nonparametric matching | Nonparametric matching / causal inference |
| 开创性文献≠ | Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34-58. 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 | Bayesian matching, Bayesian nonparametric matching, Bayes-ATE matching, posterior matching estimator | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| 相关 | 6 | 6 |
| 摘要≠ | The Bayesian Matching Estimator estimates average treatment effects in observational studies by combining classical nearest-neighbour or kernel matching with a Bayesian posterior over the treatment effect. It inherits matching's covariate-balancing logic while propagating uncertainty through a full posterior distribution rather than relying on asymptotic standard errors, yielding credible intervals that reflect both sampling variability and prior knowledge. | 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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