Bandingkan kaedah

	Penyeimbangan Entropi Spatial	Pengimbangan Entropi
Bidang	Inferens Kausal	Inferens Kausal
Keluarga	Regression model	Regression model
Tahun asal	2010s	2012
Pengasas	Extension of Hainmueller (2012) entropy balancing to spatial settings; spatial adaptations developed in geographic epidemiology and spatial econometrics literature	Jens Hainmueller
Jenis	Quasi-experimental reweighting	Covariate-balancing reweighting
Sumber perintis	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.	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.
Alias	spatial EB, geographically-weighted entropy balancing, spatial reweighting	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Berkaitan	6	6
Ringkasan	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.	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.