Comparar métodos

	Ponderación Dinámica por Probabilidad Inversa	Ponderación por Probabilidad Inversa de Tratamiento (IPW / IPTW)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1986-2000	2000
Autor original	James M. Robins and colleagues	Robins, Hernán & Brumback
Tipo	Causal weighting estimator	Causal inference weighting estimator
Fuente seminal	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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relacionados	4	5
Resumen	Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.	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.