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Analýza citlivosti na skrytou zkreslenost (Rosenbaumovy meze / E-hodnota)×Instrumentální proměnné pomocí dvoufázové metody nejmenších čtverců (IV/2SLS)×
OborKauzální inferenceKauzální inference
RodinaRegression modelRegression model
Rok vzniku20022009
TvůrcePaul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
TypSensitivity analysis for causal inferenceInstrumental-variables regression
Původní zdrojRosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Další názvyRosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivityinstrumental variables, IV estimation, 2SLS, instrumental variable regression
Příbuzné55
ShrnutíSensitivity 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).IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).
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ScholarGatePorovnat metody: Sensitivity Analysis for Unmeasured Confounding · Two-Stage Least Squares (2SLS). Získáno 2026-06-18 z https://scholargate.app/cs/compare