  We use an analogy between non-isomorphic mathematical structures defined over
the same set and the algebras induced by associative and causal levels of
information in order to argue that Reinforcement Learning, in its current
formulation, is not a causal problem, independently if the motivation behind it
has to do with an agent taking actions.
