Comparer des méthodes

	Estimateur par appariement sur données de panel	Coarsened Exact Matching (CEM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1997-2021	2011-2012
Auteur d'origine	Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension	Iacus, King, & Porro
Type	Quasi-experimental causal estimator	Matching / causal inference
Source fondatrice	Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	panel matching, matching-on-panel-data, longitudinal matching estimator, PDME	CEM, coarsened matching, monotonic imbalance bounding matching
Apparentées	6	6
Résumé	The panel data matching estimator identifies causal treatment effects by pairing each treated unit with one or more control units that share similar covariate histories in the pre-treatment periods. By exploiting the longitudinal structure of panel data, it controls for both observed time-varying confounders and stable unit characteristics, estimating the average treatment effect on the treated (ATT) without requiring a parallel-trends assumption.	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.