  The use of multiple imputation (MI) is becoming increasingly popular for
addressing missing data. Although some conventional MI approaches have been
well studied and have shown empirical validity, they have limitations when
processing large datasets with complex data structures. Their imputation
performances usually rely on the proper specification of imputation models,
which requires expert knowledge of the inherent relations among variables.
Moreover, these standard approaches tend to be computationally inefficient for
medium and large datasets. In this paper, we propose a scalable MI framework
mixgb, which is based on XGBoost, subsampling, and predictive mean matching.
Our approach leverages the power of XGBoost, a fast implementation of gradient
boosted trees, to automatically capture interactions and non-linear relations
while achieving high computational efficiency. In addition, we incorporate
subsampling and predictive mean matching to reduce bias and better account for
appropriate imputation variability. The proposed framework is implemented in an
R package mixgb. Supplementary materials for this article are available online.
