  Missing data is a prevalent issue that can significantly impair model
performance and interpretability. This paper briefly summarizes the development
of the field of missing data with respect to Explainable Artificial
Intelligence and experimentally investigates the effects of various imputation
methods on the calculation of Shapley values, a popular technique for
interpreting complex machine learning models. We compare different imputation
strategies and assess their impact on feature importance and interaction as
determined by Shapley values. Moreover, we also theoretically analyze the
effects of missing values on Shapley values. Importantly, our findings reveal
that the choice of imputation method can introduce biases that could lead to
changes in the Shapley values, thereby affecting the interpretability of the
model. Moreover, and that a lower test prediction mean square error (MSE) may
not imply a lower MSE in Shapley values and vice versa. Also, while Xgboost is
a method that could handle missing data directly, using Xgboost directly on
missing data can seriously affect interpretability compared to imputing the
data before training Xgboost. This study provides a comprehensive evaluation of
imputation methods in the context of model interpretation, offering practical
guidance for selecting appropriate techniques based on dataset characteristics
and analysis objectives. The results underscore the importance of considering
imputation effects to ensure robust and reliable insights from machine learning
models.
