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| Räumliche Propensity-Score-Übereinstimmung (Spatial Propensity Score Matching)× | Räumliche doppelt robuste Schätzung× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2000s | 2010s–2020s |
| Urheber≠ | Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward | Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature |
| Typ≠ | Quasi-experimental matching estimator | Semiparametric causal estimator |
| Wegweisende Quelle≠ | 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 ↗ | Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787. DOI ↗ |
| Aliasnamen | Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching | Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. | Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent. |
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