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Просторово-зважене оберненою ймовірністю (Spatial IPW)×Різниця різниць (Diff-in-Diff)×
ГалузьПричинно-наслідковий висновокЕконометрика
РодинаRegression modelRegression model
Рік появи2010s1994
Автор методуExtension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
ТипQuasi-experimental / causal inferenceCausal inference / panel regression
Основоположне джерелоHirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Інші назвиSpatial IPW, Geographic IPW, Spatially-weighted IPW, SIPWdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Пов'язані65
ПідсумокSpatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and control groups are balanced not only on measured covariates but also on spatial structure, enabling credible causal inference from spatially indexed observational data.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.
ScholarGateНабір даних
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ScholarGateПорівняння методів: Spatial Inverse Probability Weighting · Difference-in-Differences. Отримано 2026-06-15 з https://scholargate.app/uk/compare