方法对比

	空间中断时间序列	空间因果影响分析
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	1990s–2000s	2010s (codified)
提出者	Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s	Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988)
类型	Quasi-experimental causal inference with spatial adjustment	Quasi-experimental causal inference with spatial data
开创性文献	McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 978-0803913950	Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123-126.
别名	Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS	spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation
相关	6	4
摘要	Spatial Interrupted Time Series (Spatial ITS) extends the classic ITS design to settings where units are geo-referenced and outcomes in one location may spill over into or correlate with outcomes in neighbouring locations. It estimates the causal effect of a discrete intervention on an outcome time series while explicitly modelling geographic autocorrelation, preventing biased standard errors and enabling detection of spatial spillovers.	Spatial causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-differences or regression discontinuity with spatial econometric models, it separates the direct local effect of a treatment from indirect effects that diffuse to neighbouring areas.