# Multiple Imputation Through XGBoost

arXiv: https://arxiv.org/abs/2106.01574 · HTML: https://mlsanity.com/paper/2106.01574/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2021.

## Built on by (most cited first, 4 of 4)

- [Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studies](https://mlsanity.com/paper/2401.09602/) (2024)
- [Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study](https://mlsanity.com/paper/2412.13570/) (2024)
- [Adapting tree-based multiple imputation methods for multi-level data? A simulation study](https://mlsanity.com/paper/2401.14161/) (2024)
- [t-STEP: An interpretable model for Total Electron Content predictions and irregularities estimations](https://mlsanity.com/paper/2606.29644/) (2026)

## Abstract (arXiv)

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.