Comparer des méthodes

	Estimateur d'Appariement Dynamique	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010	2000
Auteur d'origine	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)	Robins, Hernán & Brumback
Type	Nonparametric causal inference / matching	Causal inference weighting 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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Apparentées	6	5
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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.