Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Просторово-зважене оберненою ймовірністю (Spatial IPW)× | Просторова регресія (моделі просторового лагу та просторової похибки)× | |
|---|---|---|
| Галузь≠ | Причинно-наслідковий висновок | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2010s | 1988 |
| Автор методу≠ | Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019) | Luc Anselin |
| Тип≠ | Quasi-experimental / causal inference | Spatial regression (cross-sectional) |
| Основоположне джерело≠ | Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189. DOI ↗ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. DOI ↗ |
| Інші назви≠ | Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW | spatial econometrics, spatial lag model, spatial error model, SAR / SEM |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | Spatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and control groups are balanced not only on measured covariates but also on spatial structure, enabling credible causal inference from spatially indexed observational data. | Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model, where the dependence sits in the error term. |
| ScholarGateНабір даних ↗ |
|
|