方法对比

	空间反向概率加权（空间IPW）	逆概率治疗加权法 (IPW / IPTW)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2010s	2000
提出者	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Robins, Hernán & Brumback
类型	Quasi-experimental / causal inference	Causal inference weighting estimator
开创性文献	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.
别名	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
相关	6	5
摘要	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.