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Prostorna analiza osjetljivosti za uzročnost×Metoda razlika u razlikama (engl. Difference-in-Differences, DiD)×
PodručjeUzročno zaključivanjeEkonometrija
ObiteljRegression modelRegression model
Godina nastanka1988–2021 (developed progressively)1994
TvoracAnselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworksCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
VrstaSensitivity / robustness analysisCausal inference / panel regression
Temeljni izvorAnselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Drugi nazivispatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivitydiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Srodne65
SažetakSpatial sensitivity analysis for causality systematically tests whether a causal estimate derived from georeferenced data holds up as spatial structure, spillovers, and the choice of spatial weights matrix are varied. Because nearby units often share unmeasured confounders — soil quality, local infrastructure, neighbourhood norms — a naive regression may yield biased causal estimates. This method reveals how fragile or robust a claimed causal effect is to alternative spatial specifications.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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ScholarGateUsporedite metode: Spatial Sensitivity Analysis for Causality · Difference-in-Differences. Preuzeto 2026-06-15 s https://scholargate.app/hr/compare