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| 空間的割り込み時系列分析× | 空間回帰不連続デザイン(Spatial RDD)× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1990s–2000s | 2010s |
| 提唱者≠ | 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) |
| 種類≠ | Quasi-experimental causal inference with spatial adjustment | Quasi-experimental causal inference |
| 原典≠ | 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. DOI ↗ |
| 別名 | Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS | Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design |
| 関連≠ | 6 | 4 |
| 概要≠ | 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. |
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