Comparer des méthodes

	Appariement par score de propension sur données de panel	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1997-1998	1973
Auteur d'origine	Heckman, Ichimura & Todd	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Matching / causal inference	Nonparametric matching / causal inference
Source fondatrice	Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	PSM with panel data, longitudinal PSM, panel PSM, difference-in-differences PSM	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	6	6
Résumé	Panel data propensity score matching combines the bias-reduction of PSM with the longitudinal structure of panel data, enabling causal estimation of treatment effects by matching treated and control units on observable pre-treatment characteristics and then differencing within matched pairs over time. Developed in the framework of Heckman, Ichimura, and Todd (1998), it is especially valuable when randomisation is infeasible and both selection on observables and time-varying confounding must be addressed simultaneously.	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.