Comparar métodos

	Análisis de Sensibilidad para Sesgo Oculto (Rosenbaum Bounds / E-value)	Pruebas de placebo para inferencia causal
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2002	2010
Autor original	Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)	Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity)
Tipo	Sensitivity analysis for causal inference	Falsification / robustness test family for causal inference
Fuente seminal	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.
Alias	Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity	falsification tests, placebo checks, refutation tests, Plasebo Testleri — Nedensel Çıkarım Doğrulama
Relacionados	5	5
Resumen	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).