Порівняння методів

	Маргінальна структурна модель, доповнена машинним навчанням (ML-MSM)	Зважування за показником схильності (PSW / IPW)
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model
Рік появи	2000 (MSM); 2011 (ML-augmented via targeted learning)	1983 (propensity score); 2003 (efficient IPW estimator)
Автор методу	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
Основоположне джерело	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	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.
Інші назви	ML-MSM, ML-augmented MSM, data-adaptive MSM, TMLE-MSM	PSW, inverse probability weighting, IPW, propensity-based weighting
Пов'язані	5	6
Підсумок	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).