Comparar métodos

	Ponderação por Probabilidade Inversa para Dados em Painel	Modelo Estrutural Marginal (MSM)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2000	2000
Autor original	Robins, Hernan & Brumback	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Reweighting / causal inference	Causal model / semiparametric weighting
Fonte 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., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Outros nomes	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionados	5	5
Resumo	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.	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.