Comparar métodos

	Ponderação por Escore de Propensão Multiperíodo	Modelo Estrutural Marginal (MSM)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2000	2000
Autor original	Robins, Hernán, and Brumback (building on Robins' g-computation framework)	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Quasi-experimental causal inference	Causal model / semiparametric weighting
Fonte seminal	Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Outros nomes	longitudinal propensity score weighting, multi-wave PSW, time-varying propensity score weighting, sequential propensity score weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionados	5	5
Resumo	Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments, allowing unbiased estimation of causal effects under longitudinal confounding.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.