手法を比較

	機械学習拡張回帰不連続デザイン	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	2019	1983
提唱者	Imbens & Wager (2019); Calonico, Cattaneo & Farrell (2019)	Paul Rosenbaum and Donald Rubin
種類	Causal inference / quasi-experimental	Method
原典	Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). Optimal mean squared error bandwidth selection for regression discontinuity designs. Bernoulli, 25(4A), 2703-2729.	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-RDD, ML-augmented RD, data-adaptive RDD, nonparametric RDD with ML	PSM, propensity score weighting, covariate balance
関連	3	3
概要	Machine learning-augmented regression discontinuity design (ML-RDD) combines the sharp identification logic of classical RDD — exploiting a known assignment cutoff in a running variable — with flexible, data-adaptive ML methods for bandwidth selection, conditional mean estimation, and covariate adjustment. The goal is to recover a more accurate and less assumption-laden estimate of the local average treatment effect at the threshold.	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.