Comparar métodos

	Ponderação por Escore de Propensão (PEP / IPW)	Estimativa Duplamente Robusta (AIPW)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	1983 (propensity score); 2003 (efficient IPW estimator)	2005
Autor original	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Robins & Rotnitzky; Bang & Robins
Tipo	Causal inference / reweighting	Semiparametric causal estimator
Fonte seminal	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Outros nomes	PSW, inverse probability weighting, IPW, propensity-based weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionados	6	5
Resumo	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.