手法を比較

	機械学習拡張二重頑健推定量 (ML-DR)	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	2018	1983 (propensity score); 2003 (efficient IPW estimator)
提唱者	Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
種類	Semiparametric causal estimator with ML nuisance	Causal inference / reweighting
原典	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.	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-DR, AIPW with ML, Double/Debiased ML doubly robust, DML-DR	PSW, inverse probability weighting, IPW, propensity-based weighting
関連	6	6
概要	Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners.	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).