Comparer des méthodes

	Machine Learning-Augmented Coarsened Exact Matching	Coarsened Exact Matching (CEM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2012-2019	2011-2012
Auteur d'origine	Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature	Iacus, King, & Porro
Type	Matching / quasi-experimental	Matching / causal inference
Source fondatrice	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	ML-augmented CEM, ML-CEM, automated coarsened exact matching, ML-assisted CEM	CEM, coarsened matching, monotonic imbalance bounding matching
Apparentées	6	6
Résumé	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.	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.