  We define a Causal Decision Problem as a Decision Problem where the available
actions, the family of uncertain events and the set of outcomes are related
through the variables of a Causal Graphical Model $\mathcal{G}$. A solution
criteria based on Pearl's Do-Calculus and the Expected Utility criteria for
rational preferences is proposed. The implementation of this criteria leads to
an on-line decision making procedure that has been shown to have similar
performance to classic Reinforcement Learning algorithms while allowing for a
causal model of an environment to be learned. Thus, we aim to provide the
theoretical guarantees of the usefulness and optimality of a decision making
procedure based on causal information.
