Comparar métodos

	Balanceamento de Entropia Espacial	Pareamento Espacial por Escore de Propensão
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2010s	2000s
Autor original	Extension of Hainmueller (2012) entropy balancing to spatial settings; spatial adaptations developed in geographic epidemiology and spatial econometrics literature	Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward
Tipo	Quasi-experimental reweighting	Quasi-experimental matching estimator
Fonte seminal	Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Outros nomes	spatial EB, geographically-weighted entropy balancing, spatial reweighting	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching
Relacionados	6	6
Resumo	Spatial entropy balancing extends standard entropy balancing to observational settings where units are embedded in geographic space, incorporating spatial structure into the reweighting process so that balance is achieved while respecting spatial proximity, clustering, or spillover dependencies between units.	Spatial Propensity Score Matching (Spatial PSM) extends the classic propensity score matching framework to settings where units are embedded in geographic space and treatment assignment or outcomes may be spatially correlated. By incorporating spatial covariates and adjacency structure into the propensity model and matching procedure, it produces causal estimates that account for geographic confounding and spillover effects.