方法对比

	机器学习增强的粗糙精确匹配 (ML-CEM)	熵平衡
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2012-2019	2012
提出者	Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature	Jens Hainmueller
类型	Matching / quasi-experimental	Covariate-balancing reweighting
开创性文献	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.
别名	ML-augmented CEM, ML-CEM, automated coarsened exact matching, ML-assisted CEM	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
相关	6	6
摘要	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.	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.