方法对比

	稳健反事实影响评估	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	2010s	1983–2002
提出者	European Commission evaluation community; Pellegrini, Ferrara and colleagues	Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)
类型	Robustness-validated causal evaluation	Diagnostic / robustness check
开创性文献	Bia, M., Flores, C. A., Flores-Lagunes, A., & Mattei, A. (2014). A Stata package for the application of semiparametric estimators of dose–response functions. Stata Journal, 14(3), 580–604.	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
别名	Robust CIE, Sensitivity-checked CIE, Multi-method counterfactual evaluation, Robustness-validated impact evaluation	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity
相关	5	4
摘要	Robust Counterfactual Impact Evaluation (Robust CIE) strengthens causal impact estimates by combining multiple quasi-experimental estimators, placebo tests, and formal sensitivity analyses. Rather than relying on a single method, it cross-validates findings across approaches — such as matching, difference-in-differences, and regression discontinuity — to ensure that conclusions do not depend on any single methodological choice.	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.