All Times EDT
Keywords: missing data, machine learning, ensemble learning
This paper introduces Multiple Imputation by Super Learning (MISL) to help relax the assumptions of specifying a correct imputation model while reaping the benefits of traditional multiple imputation. Through simulations, we demonstrate how MISL is able to obtain accurate parameter estimates and realistic imputed values when compared to both single and multiple imputation by chained equations regardless of the missing data mechanism.