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Ponderação por Probabilidade Inversa Espacial (Spatial IPW)×Diferenças em Diferenças (DiD)×
ÁreaInferência causalEconometria
FamíliaRegression modelRegression model
Ano de origem2010s1994
Autor originalExtension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TipoQuasi-experimental / causal inferenceCausal inference / panel regression
Fonte seminalHirano, 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
Outros nomesSpatial IPW, Geographic IPW, Spatially-weighted IPW, SIPWdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados65
ResumoSpatial 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.
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ScholarGateComparar métodos: Spatial Inverse Probability Weighting · Difference-in-Differences. Recuperado em 2026-06-15 de https://scholargate.app/pt/compare