# On the Relation between Prediction and Imputation Accuracy under Missing Covariates

arXiv: https://arxiv.org/abs/2112.05248 · HTML: https://mlsanity.com/paper/2112.05248/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Missing Value Imputation on Multidimensional Time Series](https://mlsanity.com/paper/2103.01600/) (2021)
- [Random Forest Missing Data Algorithms](https://mlsanity.com/paper/1701.05305/) (2017)
- [Learning Individual Models for Imputation (Technical Report)](https://mlsanity.com/paper/2004.03436/) (2020)
- [Asymptotic based bootstrap approach for matched pairs with missingness in a single-arm](https://mlsanity.com/paper/1912.04902/) (2019)
- [Interpretable Machines: Constructing Valid Prediction Intervals with Random Forests](https://mlsanity.com/paper/2103.05766/) (2021)

## Built on by (most cited first, 6 of 6)

- [Imputation for prediction: beware of diminishing returns](https://mlsanity.com/paper/2407.19804/) (2024)
- [Learning Causal Graphs in Manufacturing Domains using Structural Equation Models](https://mlsanity.com/paper/2210.14573/) (2022)
- [missForestPredict -- Missing data imputation for prediction settings](https://mlsanity.com/paper/2407.03379/) (2024)
- [Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study](https://mlsanity.com/paper/2412.13570/) (2024)
- [Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies](https://mlsanity.com/paper/2502.06866/) (2025)
- [Predicting missing values: A good idea?](https://mlsanity.com/paper/2605.03733/) (2026)

## Abstract (arXiv)

Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend towards the usage of modern Machine Learning algorithms for imputation. It originates from their capability of showing favourable prediction accuracy in different learning problems. In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine Learning based methods for both, imputation and prediction are used. In addition, we explore imputation performance when using statistical inference procedures in prediction settings, such as coverage rates of (valid) prediction intervals. Our analysis is based on empirical datasets provided by the UCI Machine Learning repository and an extensive simulation study.