Comparer des méthodes

	Appariement dynamique par score de propension	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1986-2010	2000
Auteur d'origine	Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching	Robins, Hernán & Brumback
Type	Sequential causal 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 PSM, sequential propensity score matching, longitudinal propensity matching, DPSM	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Apparentées	6	5
Résumé	Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments.	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.