Comparar métodos

	Balanceamento de Entropia Espacial	Ponderação por Escore de Propensão (PEP / IPW)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2010s	1983 (propensity score); 2003 (efficient IPW estimator)
Autor original	Extension of Hainmueller (2012) entropy balancing to spatial settings; spatial adaptations developed in geographic epidemiology and spatial econometrics literature	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tipo	Quasi-experimental reweighting	Causal inference / reweighting
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	PSW, inverse probability weighting, IPW, propensity-based weighting
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.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).