Comparer des méthodes

	Estimateur d'Appariement Dynamique	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010	1973
Auteur d'origine	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Nonparametric causal inference / matching	Nonparametric matching / causal inference
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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME	nearest-neighbor matching, NNM, matching on covariates, covariate matching
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 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.