方法对比

	空间中断时间序列	空间回归不连续设计 (Spatial RDD)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	1990s–2000s	2010s
提出者	Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
类型	Quasi-experimental causal inference with spatial adjustment	Quasi-experimental causal inference
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	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
别名	Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
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 Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.