Comparer des méthodes

	Estimateur par appariement multi-périodes	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2005	1973
Auteur d'origine	Abadie (2005); Imbens & Wooldridge (2009)	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Quasi-experimental / causal inference	Nonparametric matching / causal inference
Source fondatrice	Abadie, A. (2005). Semiparametric Difference-in-Differences Estimators. Review of Economic Studies, 72(1), 1-19.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	panel matching estimator, longitudinal matching, multi-wave matching, repeated-cross-section matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	6	6
Résumé	The multi-period matching estimator extends the standard matching framework to settings with multiple time periods, pairing each treated unit to similar untreated units based on pre-treatment covariates or propensity scores, then using within-pair before-after differences to estimate the average treatment effect on the treated (ATT). Leveraging repeated observations, it simultaneously controls for observed confounders and time-invariant unobserved heterogeneity.	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.