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Analisis Sensitivitas untuk Kausalitas×Perbedaan-dalam-Perbedaan (Diff-in-Diff)×
BidangInferensi KausalEkonometrika
KeluargaRegression modelRegression model
Tahun asal1983–20021994
PencetusPaul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TipeDiagnostic / robustness checkCausal inference / panel 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
Aliassensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivitydiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Terkait45
RingkasanSensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis.Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.
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ScholarGateBandingkan metode: Sensitivity Analysis for Causality · Difference-in-Differences. Diakses 2026-06-15 dari https://scholargate.app/id/compare