Abstract: Decision trees are important both as interpretable models, amenable to high-stakes decision-making, and as building blocks of ensemble methods such as random forests and gradient boosting. Their statistical properties, however, are not yet well understood. In particular, it is unclear why there is sometimes a prediction performance gap between them and powerful but uninterpretable...