Comparar métodos

	Robust Inverse Probability Weighting (Robust IPW)	Modelo Estrutural Marginal (MSM)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2000-2004	2000
Autor original	Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Causal weighting estimator	Causal model / semiparametric weighting
Fonte seminal	Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Outros nomes	Robust IPW, Stabilized IPW, Trimmed IPW, Variance-robust IPW	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionados	5	5
Resumo	Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to improve finite-sample performance and inferential reliability in observational studies.	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.