Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Spatial Matching Estimator× | Tilajallinen taipumusmallin sovitus (Spatial Propensity Score Matching)× | |
|---|---|---|
| Tieteenala | Kausaalipäättely | Kausaalipäättely |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2000s–2010s | 2000s |
| Kehittäjä≠ | Extension of Abadie & Imbens (2006) matching estimator to spatial settings; geographic applications developed in urban/environmental econometrics literature | Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward |
| Tyyppi≠ | Quasi-experimental causal inference | Quasi-experimental matching estimator |
| Alkuperäislähde≠ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ | 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 ↗ |
| Rinnakkaisnimet | geographic matching estimator, spatial nearest-neighbor matching, location-based matching estimator, spatially-weighted matching | Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | The Spatial Matching Estimator estimates causal treatment effects by pairing each treated geographic unit with one or more similar untreated units nearby, exploiting the assumption that units close in space share similar unobserved characteristics. By restricting matches to a geographic neighbourhood or weighting by spatial proximity, the method controls for location-specific confounders that standard matching ignores. | 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. |
| ScholarGateAineisto ↗ |
|
|