Comparar métodos

	Balanceo de entropía espacial	Estimación Espacialmente Doblemente Robusta
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2010s	2010s–2020s
Autor original	Extension of Hainmueller (2012) entropy balancing to spatial settings; spatial adaptations developed in geographic epidemiology and spatial econometrics literature	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature
Tipo	Quasi-experimental reweighting	Semiparametric causal estimator
Fuente 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.	Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787.
Alias	spatial EB, geographically-weighted entropy balancing, spatial reweighting	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation
Relacionados	6	5
Resumen	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 doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent.