Comparer des méthodes

	Estimateur d'Appariement Dynamique	Estimateur par appariement sur données de panel
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010	1997-2021
Auteur d'origine	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)	Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension
Type	Nonparametric causal inference / matching	Quasi-experimental causal estimator
Source fondatrice	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.	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.
Alias	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME	panel matching, matching-on-panel-data, longitudinal matching estimator, PDME
Apparentées	6	6
Résumé	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.	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.