Comparer des méthodes

	Pondération par l'inverse de la probabilité sur plusieurs périodes	Pondération par l'inverse de la probabilité pour données de panel
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000	2000
Auteur d'origine	Robins, Hernan & Brumback	Robins, Hernan & Brumback
Type	Weighted causal estimator	Reweighting / causal inference
Source fondatrice	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW
Apparentées	6	5
Résumé	Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies.	Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.