  Missing values are a fundamental problem in data science. Many datasets have
missing values that must be properly handled because the way missing values are
treated can have large impact on the resulting machine learning model. In
medical applications, the consequences may affect healthcare decisions. There
are many methods in the literature for dealing with missing values, including
state-of-the-art methods which often depend on black-box models for imputation.
In this work, we show how recent advances in interpretable machine learning
provide a new perspective for understanding and tackling the missing value
problem. We propose methods based on high-accuracy glass-box Explainable
Boosting Machines (EBMs) that can help users (1) gain new insights on
missingness mechanisms and better understand the causes of missingness, and (2)
detect -- or even alleviate -- potential risks introduced by imputation
algorithms. Experiments on real-world medical datasets illustrate the
effectiveness of the proposed methods.
