Comparar métodos

	Heterogeneous Treatment Effect Inverse Probability Weighting	Ponderação pela Probabilidade Inversa de Tratamento (IPW / IPTW)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2003–2015	2000
Autor original	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Robins, Hernán & Brumback
Tipo	Causal inference / weighted regression	Causal inference weighting estimator
Fonte seminal	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Outros nomes	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relacionados	5	5
Resumo	HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics.	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.