  Multiple imputation (MI) is the state-of-the-art approach for dealing with
missing data arising from non-response in sample surveys. Multiple imputation
by chained equations (MICE) is the most widely used MI method, but it lacks
theoretical foundation and is computationally intensive. Recently, MI methods
based on deep learning models have been developed with encouraging results in
small studies. However, there has been limited research on systematically
evaluating their performance in realistic settings comparing to MICE,
particularly in large-scale surveys. This paper provides a general framework
for using simulations based on real survey data and several performance metrics
to compare MI methods. We conduct extensive simulation studies based on the
American Community Survey data to compare repeated sampling properties of four
machine learning based MI methods: MICE with classification trees, MICE with
random forests, generative adversarial imputation network, and multiple
imputation using denoising autoencoders. We find the deep learning based MI
methods dominate MICE in terms of computational time; however, MICE with
classification trees consistently outperforms the deep learning MI methods in
terms of bias, mean squared error, and coverage under a range of realistic
settings.
