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Dealing with missing data is an important problem in statistical analysisthat is often addressed with imputation procedures. The performance andvalidity of such methods are of great importance for their application inempirical studies. While the prevailing method of Multiple Imputation byChained Equations (MICE) with Predictive Mean Matching (PMM) is consideredstandard in the social science literature, the increase in complex datasets mayrequire 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 forinference in linear models. In this study, we investigate the impact of variousimputation methods on coefficient estimation, Type I error, and power, to gaininsights that can help empirical researchers deal with missingness moreeffectively. We explore MICE PMM alongside different tree-based methods, suchas MICE with Random Forest (RF), Chained Random Forests with and without PMM(missRanger), and Extreme Gradient Boosting (MIXGBoost), conducting a realisticsimulation study using the German National Educational Panel Study (NEPS) asthe original data source. Our results reveal that Random Forest-basedimputations, especially MICE RF and missRanger with PMM, consistently performbetter in most scenarios. Standard MICE PMM shows partially increased bias andoverly conservative test decisions, particularly with non-true zerocoefficients. Our results thus underscore the potential advantages oftree-based imputation methods, albeit with a caveat that all methods performworse with an increased missingness, particularly missRanger.

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