  We extend the empirical results from the structural equation model (SEM)
published in the paper Assortment Planning for Retail Buying, Retail Store
Operations, and Firm Performance [1] by implementing the directed acyclic graph
as a causal Bayesian neural network. Neural network convergence is shown to
improve with the removal of the node with the weakest SEM path when variational
inference is provided by perturbing weights with Flipout layers, while results
from perturbing weights at the output with the Vadam optimizer are
inconclusive.
