方法对比
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| 教育研究中因果关系的敏感性分析× | 匹配方法（CEM / 最优 / 遗传）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1983–2002 | 2012 |
| 提出者≠ | Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others | Iacus, King & Porro (CEM); Hansen (optimal/full matching) |
| 类型≠ | Causal robustness / bias assessment | Matching for causal inference |
| 开创性文献≠ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 | 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≠ | Rosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studies | coarsened exact matching, optimal matching, genetic matching, CEM |
| 相关≠ | 6 | 5 |
| 摘要≠ | Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings. | 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. |
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