  Missing data are present in most real world problems and need careful
handling to preserve the prediction accuracy and statistical consistency in the
downstream analysis. As the gold standard of handling missing data, multiple
imputation (MI) methods are proposed to account for the imputation uncertainty
and provide proper statistical inference.
  In this work, we propose Multiple Imputation via Generative Adversarial
Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple
imputation method, that can work under missing at random (MAR) mechanism with
theoretical support. MI-GAN leverages recent progress in conditional generative
adversarial neural works and shows strong performance matching existing
state-of-the-art imputation methods on high-dimensional datasets, in terms of
imputation error. In particular, MI-GAN significantly outperforms other
imputation methods in the sense of statistical inference and computational
speed.
