手法を比較

	機械学習拡張型粗視化完全一致法（ML-CEM）	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	2012-2019	1983
提唱者	Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature	Paul Rosenbaum and Donald Rubin
種類	Matching / quasi-experimental	Method
原典	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	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-augmented CEM, ML-CEM, automated coarsened exact matching, ML-assisted CEM	PSM, propensity score weighting, covariate balance
関連	6	3
概要	Machine Learning-Augmented Coarsened Exact Matching extends Coarsened Exact Matching (Iacus, King & Porro, 2012) by using supervised machine learning to automate and optimise the coarsening step — the discretisation of continuous covariates into bins — rather than relying on researcher-specified cutpoints. This reduces both ad hoc subjectivity in coarsening decisions and residual imbalance, while preserving CEM's core logic of exact matching within coarsened strata.	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.