Comparer des méthodes

	Machine Learning-Augmented Coarsened Exact Matching	Appariement par score de propension
Domaine	Inférence causale	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	2012-2019	1983
Auteur d'origine	Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature	Paul Rosenbaum and Donald Rubin
Type	Matching / quasi-experimental	Method
Source fondatrice	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.
Alias	ML-augmented CEM, ML-CEM, automated coarsened exact matching, ML-assisted CEM	PSM, propensity score weighting, covariate balance
Apparentées	6	3
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.	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.