方法对比
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| 异质性处理效应的安慰剂检验× | 因果关系的敏感性分析× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2000s–2010s | 1983–2002 |
| 提出者≠ | Rosenbaum (placebo test concept); Athey & Imbens (HTE estimation framework) | Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach) |
| 类型≠ | Validation / falsification test | Diagnostic / robustness check |
| 开创性文献≠ | Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. ISBN: 978-0521885881 | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| 别名 | HTE placebo test, heterogeneous-effect placebo check, subgroup placebo test, CATE placebo validation | sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity |
| 相关≠ | 3 | 4 |
| 摘要≠ | A placebo test for heterogeneous treatment effects is a falsification strategy used to validate whether estimated variation in treatment effects across subgroups or covariate values is genuine rather than an artifact of model specification, overfitting, or coincidental patterns. By applying the same estimation procedure to pseudo-treatments, fake outcomes, or subgroups that logically should not differ, researchers check that observed heterogeneity reflects real causal variation. | Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis. |
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