Bandingkan kaedah

	Penyeimbangan Entropi Spatial	Penimbang Skor Kecenderungan (PSW / IPW)
Bidang	Inferens Kausal	Inferens Kausal
Keluarga	Regression model	Regression model
Tahun asal	2010s	1983 (propensity score); 2003 (efficient IPW estimator)
Pengasas	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)
Jenis	Quasi-experimental reweighting	Causal inference / 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.	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.
Alias	spatial EB, geographically-weighted entropy balancing, spatial reweighting	PSW, inverse probability weighting, IPW, propensity-based weighting
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.	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).