手法を比較

	異質的処置効果周辺構造モデル（HTE-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	2000–2010s	1983 (propensity score); 2003 (efficient IPW estimator)
提唱者	Robins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010s	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
種類	Causal inference / weighted regression with effect modification	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	HTE-MSM, heterogeneous MSM, subgroup MSM, effect-modified marginal structural model	PSW, inverse probability weighting, IPW, propensity-based weighting
関連	5	6
概要	The Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations with inverse probability of treatment weights (IPTW) and interacting the treatment with effect modifiers in the weighted outcome model, the approach produces subgroup-specific or continuous causal effect estimates from observational data.	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).