Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Просторово подвійно робастна оцінка× | Зіставлення за показником схильності× | |
|---|---|---|
| Галузь≠ | Причинно-наслідковий висновок | Статистика досліджень |
| Родина≠ | Regression model | Process / pipeline |
| Рік появи≠ | 2010s–2020s | 1983 |
| Автор методу≠ | Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature | Paul Rosenbaum and Donald Rubin |
| Тип≠ | Semiparametric causal estimator | Method |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation | PSM, propensity score weighting, covariate balance |
| Пов'язані≠ | 5 | 3 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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