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Sensitivitätsanalyse auf verborgene Verzerrungen (Rosenbaum-Schranken / E-Wert)×Lokaler durchschnittlicher Behandlungseffekt (LATE / CACE)×
FachgebietKausale InferenzKausale Inferenz
FamilieRegression modelRegression model
Entstehungsjahr20021994
UrheberPaul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
TypSensitivity analysis for causal inferenceInstrumental-variable causal estimand
Wegweisende QuelleRosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475. DOI ↗
AliasnamenRosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivityLATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
Verwandt55
ZusammenfassungSensitivity 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).The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.
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ScholarGateMethoden vergleichen: Sensitivity Analysis for Unmeasured Confounding · Local Average Treatment Effect. Abgerufen am 2026-06-17 von https://scholargate.app/de/compare