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| Przestrzenna estymacja podwójnie odporna× | Dopasowanie wyników skłonności× | |
|---|---|---|
| Dziedzina≠ | Wnioskowanie przyczynowe | Statystyka w badaniach |
| Rodzina≠ | Regression model | Process / pipeline |
| Rok powstania≠ | 2010s–2020s | 1983 |
| Twórca≠ | Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature | Paul Rosenbaum and Donald Rubin |
| Typ≠ | Semiparametric causal estimator | Method |
| Źródło pierwotne≠ | 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 ↗ | 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 ↗ |
| Inne nazwy≠ | Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation | PSM, propensity score weighting, covariate balance |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | 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. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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