Compara mètodes

	Model Estructural Marginal per a l'Avaluació de Polítiques	Pes pesat per la probabilitat inversa (IPW / IPTW)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2000	2000
Autor original	James M. Robins, Miguel A. Hernan, Babette Brumback	Robins, Hernán & Brumback
Tipus	Causal inference / weighted regression	Causal inference weighting estimator
Font 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.
Àlies	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relacionats	6	5
Resum	A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data.	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.