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。
| 面板数据安慰剂检验× | 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≠ | 2004-2010 | 1983–2002 |
| 提出者≠ | Bertrand, Duflo & Mullainathan; Abadie, Diamond & Hainmueller | Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach) |
| 类型≠ | Falsification / validation test | Diagnostic / robustness check |
| 开创性文献≠ | Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1), 249-275. DOI ↗ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| 别名 | placebo regression test, falsification test, pseudo-treatment test, in-time placebo | sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity |
| 相关 | 4 | 4 |
| 摘要≠ | A panel data placebo test is a falsification procedure used to assess the credibility of causal estimates in quasi-experimental panel designs. By applying the same estimation strategy to a period, group, or outcome where no true effect should exist, researchers verify that the observed treatment effect is not merely an artifact of model specification, coincidental trends, or data patterns unrelated to the intervention. | 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. |
| ScholarGate数据集 ↗ |
|
|