  Bayesian Networks may be appealing for clinical decision-making due to their
inclusion of causal knowledge, but their practical adoption remains limited as
a result of their inability to deal with unstructured data. While neural
networks do not have this limitation, they are not interpretable and are
inherently unable to deal with causal structure in the input space. Our goal is
to build neural networks that combine the advantages of both approaches.
Motivated by the perspective to inject causal knowledge while training such
neural networks, this work presents initial steps in that direction. We
demonstrate how a neural network can be trained to output conditional
probabilities, providing approximately the same functionality as a Bayesian
Network. Additionally, we propose two training strategies that allow encoding
the independence relations inferred from a given causal structure into the
neural network. We present initial results in a proof-of-concept setting,
showing that the neural model acts as an understudy to its Bayesian Network
counterpart, approximating its probabilistic and causal properties.
