  There are now many explainable AI methods for understanding the decisions of
a machine learning model. Among these are those based on counterfactual
reasoning, which involve simulating features changes and observing the impact
on the prediction. This article proposes to view this simulation process as a
source of creating a certain amount of knowledge that can be stored to be used,
later, in different ways. This process is illustrated in the additive model
and, more specifically, in the case of the naive Bayes classifier, whose
interesting properties for this purpose are shown.
