  Multiple imputation (MI) is a popular approach for dealing with missing data
arising from non-response in sample surveys. Multiple imputation by chained
equations (MICE) is one of the most widely used MI algorithms for multivariate
data, but it lacks theoretical foundation and is computationally intensive.
Recently, missing data imputation methods based on deep learning models have
been developed with encouraging results in small studies. However, there has
been limited research on evaluating their performance in realistic settings
compared to MICE, particularly in big surveys. We conduct extensive simulation
studies based on a subsample of the American Community Survey to compare the
repeated sampling properties of four machine learning based MI methods: MICE
with classification trees, MICE with random forests, generative adversarial
imputation networks, and multiple imputation using denoising autoencoders. We
find the deep learning imputation methods are superior to MICE in terms of
computational time. However, with the default choice of hyperparameters in the
common software packages, MICE with classification trees consistently
outperforms, often by a large margin, the deep learning imputation methods in
terms of bias, mean squared error, and coverage under a range of realistic
settings.
