Structural Learning of Simple Staged Trees
This work addresses the challenge of visualizing and modeling non-symmetric dependencies in high-dimensional categorical data, offering a novel method for statisticians and data scientists.
The authors tackled the problem of learning non-symmetric conditional independences in categorical data by introducing the first structural learning algorithms for simple staged trees, which outperform Bayesian networks in model fit and provide a compact graph for easy interpretation.
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric conditional independences via vertex coloring. However, since they are based on a tree representation of the sample space, the underlying graph becomes cluttered and difficult to visualize as the number of variables increases. Here we introduce the first structural learning algorithms for the class of simple staged trees, entertaining a compact coalescence of the underlying tree from which non-symmetric independences can be easily read. We show that data-learned simple staged trees often outperform Bayesian networks in model fit and illustrate how the coalesced graph is used to identify non-symmetric conditional independences.