Compara mètodes

	Equilibratge Entròpic Espacial	Parellament Espacial de Puntuacions de Propensió
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	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
Tipus	Quasi-experimental reweighting	Quasi-experimental matching estimator
Font 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.
Àlies	spatial EB, geographically-weighted entropy balancing, spatial reweighting	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching
Relacionats	6	6
Resum	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.