手法を比較

	傾向スコア重み付け（PSW / IPW）	傾向スコアマッチング
分野	因果推論	研究統計
系統	Regression model	Process / pipeline
提唱年	1983 (propensity score); 2003 (efficient IPW estimator)	1983
提唱者	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Paul Rosenbaum and Donald Rubin
種類	Causal inference / reweighting	Method
原典	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.	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	PSW, inverse probability weighting, IPW, propensity-based weighting	PSM, propensity score weighting, covariate balance
関連	6	3
概要	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).	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.