手法を比較

	マッチング手法（CEM / 最適 / ジェネティック）	異質的処置効果（CATE / メタ学習器）
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	2012	2018
提唱者	Iacus, King & Porro (CEM); Hansen (optimal/full matching)	Wager & Athey (causal forest); Künzel et al. (meta-learners)
種類	Matching for causal inference	Causal machine-learning framework
原典	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.
別名	coarsened exact matching, optimal matching, genetic matching, CEM	conditional average treatment effect, CATE, meta-learners, causal forest
関連	5	5
概要	Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching.	Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (2018) and Künzel et al. (2019).