Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пространственный анализ прерванных временных рядов× | Spatial Propensity Score Matching× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990s–2000s | 2000s |
| Автор метода≠ | Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s | Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward |
| Тип≠ | Quasi-experimental causal inference with spatial adjustment | Quasi-experimental matching estimator |
| Основополагающий источник≠ | McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 978-0803913950 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗ |
| Другие названия | Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS | Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching |
| Связанные | 6 | 6 |
| Сводка≠ | 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 Propensity Score Matching (Spatial PSM) extends the classic propensity score matching framework to settings where units are embedded in geographic space and treatment assignment or outcomes may be spatially correlated. By incorporating spatial covariates and adjacency structure into the propensity model and matching procedure, it produces causal estimates that account for geographic confounding and spillover effects. |
| ScholarGateНабор данных ↗ |
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