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Analisis Sensitivitas untuk Bias Tersembunyi (Batas Rosenbaum / Nilai-E)×Variabel Instrumental melalui Kuadrat Terkecil Dua Tahap (IV/2SLS)×
BidangInferensi KausalInferensi Kausal
KeluargaRegression modelRegression model
Tahun asal20022009
PencetusPaul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
TipeSensitivity analysis for causal inferenceInstrumental-variables regression
Sumber perintisRosenbaum, 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
AliasRosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivityinstrumental variables, IV estimation, 2SLS, instrumental variable regression
Terkait55
RingkasanSensitivity 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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ScholarGateBandingkan metode: Sensitivity Analysis for Unmeasured Confounding · Two-Stage Least Squares (2SLS). Diakses 2026-06-18 dari https://scholargate.app/id/compare