مقایسهٔ روشها
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| مدل ساختاری حاشیهای تقویتشده با یادگیری ماشین (ML-MSM)× | برآورد دوگانه استوار (AIPW)× | |
|---|---|---|
| حوزه | استنتاج علّی | استنتاج علّی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 2000 (MSM); 2011 (ML-augmented via targeted learning) | 2005 |
| پدیدآور≠ | Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011) | Robins & Rotnitzky; Bang & Robins |
| نوع≠ | Causal inference / semiparametric weighted regression | Semiparametric causal estimator |
| منبع بنیادین≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ |
| نامهای دیگر | ML-MSM, ML-augmented MSM, data-adaptive MSM, TMLE-MSM | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| مرتبط | 5 | 5 |
| خلاصه≠ | 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. | Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified. |
| ScholarGateمجموعهداده ↗ |
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