  Staged trees are a relatively recent class of probabilistic graphical models
that extend Bayesian networks to formally and graphically account for
non-symmetric patterns of dependence. Machine learning algorithms to learn them
from data have been implemented in various pieces of software. However, to
date, methods to assess the robustness and validity of the learned,
non-symmetric relationships are not available. Here, we introduce validation
techniques tailored to staged tree models based on non-parametric bootstrap
resampling methods and investigate their use in practical applications. In
particular, we focus on the evaluation of transport services using large-scale
survey data. In these types of applications, data from heterogeneous sources
must be collated together. Staged trees provide a natural framework for this
integration of data and its analysis. For the thorough evaluation of transport
services, we further implement novel what-if sensitivity analyses for staged
trees and their visualization using software.
