Augmented Inverse Probability Weighting

The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data

Augmented Inverse Probability Weighting (AIPW) is a IPW technique that derives estimators using a combination of the propensity score and the regression model. This approach has the attractive doubly robust property that estimators are consistent as long as either the propensity score or the outcome regression model is correctly specified

Weighting to compensate for nonresponse attaches weights to subjects included in the analysis to restore the representation in the original sample which is distorted because of missing values