Comparar métodos

	Ponderação por Probabilidade Inversa em Pesquisa Educacional	Estimativa Duplamente Robusta (AIPW)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	1983–2003	2005
Autor original	Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003)	Robins & Rotnitzky; Bang & Robins
Tipo	Causal weighting estimator	Semiparametric causal 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. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Outros nomes	IPW in education, propensity-weighted analysis, IPTW education, inverse probability treatment weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionados	6	5
Resumo	Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they received the treatment — thereby creating a pseudo-population in which programme participation is independent of measured background characteristics. The method is widely used in education research to evaluate school programmes, interventions, and policies from administrative or survey data.	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.