  Decision trees are a popular family of models due to their attractive
properties such as interpretability and ability to handle heterogeneous data.
Concurrently, missing data is a prevalent occurrence that hinders performance
of machine learning models. As such, handling missing data in decision trees is
a well studied problem. In this paper, we tackle this problem by taking a
probabilistic approach. At deployment time, we use tractable density estimators
to compute the "expected prediction" of our models. At learning time, we
fine-tune parameters of already learned trees by minimizing their "expected
prediction loss" w.r.t.\ our density estimators. We provide brief experiments
showcasing effectiveness of our methods compared to few baselines.
