قارن الطرق

	الترجيح العكسي الاحتمالي المكاني (Spatial IPW)	الترجيح الاحتمالي العكسي (IPW / IPTW)
المجال	الاستدلال السببي	الاستدلال السببي
العائلة	Regression model	Regression model
سنة النشأة	2010s	2000
صاحب الطريقة	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Robins, Hernán & Brumback
النوع	Quasi-experimental / causal inference	Causal inference weighting estimator
المصدر التأسيسي	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
الأسماء البديلة	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
ذات صلة	6	5
الملخص	Spatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and control groups are balanced not only on measured covariates but also on spatial structure, enabling credible causal inference from spatially indexed observational data.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.