Comparar métodos

	Estimativa Duplamente Robusta para Avaliação de Políticas	Estimativa Duplamente Robusta (AIPW)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	1994-2005	2005
Autor original	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	Robins & Rotnitzky; Bang & Robins
Tipo	Semiparametric causal estimator	Semiparametric causal estimator
Fonte seminal	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	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	DR estimation for policy, augmented IPW for policy evaluation, AIPW policy evaluation, doubly robust policy analysis	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionados	5	5
Resumo	Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation.	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.