方法对比

	空间倾向得分加权	空间双重差分法
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2000s–2010s	2015
提出者	Extended from Hirano, Imbens & Ridder (2003) IPTW with spatial adaptations by Keele, Titiunik and others in geographically structured causal designs	Delgado & Florax
类型	Quasi-experimental / causal inference	Quasi-experimental estimator
开创性文献	Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.	Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 126, 35–40.
别名	spatial PSW, geographically weighted propensity score weighting, spatial IPTW, spatially adjusted inverse probability weighting	Spatial DiD, Geo-DiD, Difference-in-Differences with Spatial Autocorrelation, Mekansal Fark-içinde-Farklar
相关	6	3
摘要	Spatial propensity score weighting extends inverse probability of treatment weighting (IPTW) to settings where units are geographically located and treatment assignment may depend on spatial factors such as location, neighborhood characteristics, or spatial clustering. By incorporating spatial covariates into the propensity score model and adjusting standard errors for spatial autocorrelation, it produces more credible causal estimates from observational geographic data.	Spatial Difference-in-Differences (Spatial DiD) extends the classical DiD estimator to settings where observations are geo-referenced and outcomes may be spatially autocorrelated or subject to spillover effects. Introduced by Delgado and Florax (2015), the method augments the standard two-way fixed-effects DiD regression with a spatial lag or spatial error term, yielding unbiased treatment-effect estimates even when policy shocks propagate across geographic units. It is used by economists, regional scientists, and urban planners evaluating place-based interventions such as infrastructure investment, environmental regulations, or zoning reforms.