Comparar métodos

	Robust Inverse Probability Weighting (Robust IPW)	Ponderação pela Probabilidade Inversa de Tratamento (IPW / IPTW)
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)	Robins, Hernán & Brumback
Tipo	Causal weighting estimator	Causal inference weighting estimator
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., Hernán, 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	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
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.	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.