Comparer des méthodes

	Appariement exact "coarsened" multi-périodes	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2012–2021	1973
Auteur d'origine	Iacus, King & Porro (CEM, 2012); extended to multi-period panel settings	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Non-parametric matching / causal inference	Nonparametric 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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	Multi-period CEM, Longitudinal CEM, Panel CEM, Multi-wave CEM	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	6	6
Résumé	Multi-period Coarsened Exact Matching (multi-period CEM) extends the CEM framework of Iacus, King, and Porro to longitudinal data with multiple pre- and post-treatment periods. It bins continuous covariates into coarsened categories, matches treated and control units that fall into the same cells across all relevant time periods, and then estimates a weighted average treatment effect that accounts for temporal structure.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.