Comparer des méthodes

	Séries chronologiques interrompues spatiales	Appariement par Score de Propension Spatiale
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	2000s
Auteur d'origine	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
Type	Quasi-experimental causal inference with spatial adjustment	Quasi-experimental matching estimator
Source fondatrice	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.
Alias	Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching
Apparentées	6	6
Résumé	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.