手法を比較

	機械学習強化型周辺構造モデル（ML-MSM）	傾向スコア重み付け（PSW / IPW）
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	2000 (MSM); 2011 (ML-augmented via targeted learning)	1983 (propensity score); 2003 (efficient IPW estimator)
提唱者	Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
種類	Causal inference / semiparametric weighted regression	Causal inference / reweighting
原典	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.
別名	ML-MSM, ML-augmented MSM, data-adaptive MSM, TMLE-MSM	PSW, inverse probability weighting, IPW, propensity-based weighting
関連	5	6
概要	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).