Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Машинно-обучаемая дополненная маргинальная структурная модель (ML-MSM)× | Взвешивание на основе оценки склонности (PSW / IPW)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2000 (MSM); 2011 (ML-augmented via targeted learning) | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Автор метода≠ | Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Тип≠ | Causal inference / semiparametric weighted regression | Causal inference / reweighting |
| Основополагающий источник≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. 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 ↗ |
| Другие названия | ML-MSM, ML-augmented MSM, data-adaptive MSM, TMLE-MSM | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Связанные≠ | 5 | 6 |
| Сводка≠ | The machine learning-augmented marginal structural model combines the causal rigour of Robins et al.'s MSM framework with flexible, data-adaptive ML algorithms for estimating propensity scores and outcome models. By replacing parametric nuisance models with ensemble learners or neural networks, ML-MSMs recover valid causal estimates under confounding without relying on correctly specified parametric forms. | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). |
| ScholarGateНабор данных ↗ |
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