Multiple Imputation Through XGBoost

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, and this 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 nonlinear relations while achieving high computational efficiency. In addition, we incorporate subsampling and predictive mean matching to reduce bias and to better account for appropriate imputation variability. The proposed framework is implemented in an R package mixgb. Supplementary materials for this article are available online.多重插补（multiple imputation, MI）在缺失数据处理领域的应用正日益普及。尽管部分传统多重插补方法已得到充分研究并展现出良好的实证效度，但在处理具备复杂数据结构的大型数据集时仍存在局限。此类方法的插补性能通常依赖于插补模型的正确设定，而这需要掌握变量间内在关联的专业知识。此外，针对中大型数据集，这类标准方法往往计算效率低下。 本文提出一种基于XGBoost、子采样与预测均值匹配的可扩展多重插补框架mixgb。该方法借助梯度提升树的高效实现工具XGBoost的强大性能，可自动捕捉交互效应与非线性关系，同时具备优异的计算效率。此外，我们引入子采样与预测均值匹配以降低偏差，并更好地适配合理的插补变异性。所提框架已通过R包mixgb实现。本文的补充材料可在线获取。