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Sensitivity Analysis for Unmeasured Confounding

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).

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Sensitivity Analysis for Hidden Bias in Observational Studies (Rosenbaum Bounds / E-value)

Taxonomisk metodpost · regression-model / causal-inference

Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. · ISBN 978-0387989679
VanderWeele, T. J. & Ding, P. (2017). Sensitivity Analysis in Observational Research: Introducing the E-Value. Annals of Internal Medicine, 167(4), 268-274. · DOI 10.7326/M16-2607

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