Comparer des méthodes

	Estimateur par appariement multi-périodes	Estimateur d'Appariement Dynamique
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2005	2010
Auteur d'origine	Abadie (2005); Imbens & Wooldridge (2009)	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)
Type	Quasi-experimental / causal inference	Nonparametric causal inference / matching
Source fondatrice	Abadie, A. (2005). Semiparametric Difference-in-Differences Estimators. Review of Economic Studies, 72(1), 1-19.	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.
Alias	panel matching estimator, longitudinal matching, multi-wave matching, repeated-cross-section matching	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME
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 Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions.