方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 对隐藏偏差的敏感性分析（Rosenbaum 界 / E 值）× | 因果推断中的安慰剂检验× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2002 | 2010 |
| 提出者≠ | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) | Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity) |
| 类型≠ | Sensitivity analysis for causal inference | Falsification / robustness test family for causal inference |
| 开创性文献≠ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 | Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗ |
| 别名≠ | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity | falsification tests, placebo checks, refutation tests, Plasebo Testleri — Nedensel Çıkarım Doğrulama |
| 相关 | 5 | 5 |
| 摘要≠ | Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017). | Placebo tests are a family of falsification checks that probe the credibility of a causal claim by re-running the analysis on a fake treatment, a false intervention date, or an outcome that should not have been affected. The approach was popularised through the synthetic control work of Abadie, Diamond and Hainmueller (2010) and the regression-discontinuity validity checks of Imbens and Lemieux (2008). |
| ScholarGate数据集 ↗ |
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