Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studies

TL;DR

This study compares tree-based imputation methods to the standard MICE PMM approach for handling missing data, focusing on their impact on statistical inference in empirical research, using a large educational dataset.

Contribution

It provides a comprehensive evaluation of tree-based imputation methods versus MICE PMM, highlighting their relative performance in coefficient estimation, error rates, and power in linear models.

Findings

Random Forest-based imputations outperform MICE PMM in most scenarios.

MICE PMM shows increased bias and conservative test decisions.

All methods perform worse with higher missingness, especially missRanger.

Abstract

Dealing with missing data is an important problem in statistical analysis that is often addressed with imputation procedures. The performance and validity of such methods are of great importance for their application in empirical studies. While the prevailing method of Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching (PMM) is considered standard in the social science literature, the increase in complex datasets may require more advanced approaches based on machine learning. In particular, tree-based imputation methods have emerged as very competitive approaches. However, the performance and validity are not completely understood, particularly compared to the standard MICE PMM. This is especially true for inference in linear models. In this study, we investigate the impact of various imputation methods on coefficient estimation, Type I error, and power, to gain insights that can help empirical researchers deal with missingness more effectively. We explore MICE PMM alongside different tree-based methods, such as MICE with Random Forest (RF), Chained Random Forests with and without PMM (missRanger), and Extreme Gradient Boosting (MIXGBoost), conducting a realistic simulation study using the German National Educational Panel Study (NEPS) as the original data source. Our results reveal that Random Forest-based imputations, especially MICE RF and missRanger with PMM, consistently perform better in most scenarios. Standard MICE PMM shows partially increased bias and overly conservative test decisions, particularly with non-true zero coefficients. Our results thus underscore the potential advantages of tree-based imputation methods, albeit with a caveat that all methods perform worse with an increased missingness, particularly missRanger.