  Decision-making under uncertainty and causal thinking are fundamental aspects
of intelligent reasoning. Decision-making has been well studied when the
available information is considered at the associative (probabilistic) level.
The classical Theorems of von Neumann-Morgenstern and Savage provide a formal
criterion for rational choice using associative information: maximize expected
utility. There is an ongoing debate around the origin of probabilities involved
in such calculation. In this work, we will show how the probabilities for
decision-making can be grounded in causal models by considering decision
problems in which the available actions and consequences are causally
connected. In this setting, actions are regarded as an intervention over a
causal model. Then, we extend a previous causal decision-making result, which
relies on a known causal model, to the case in which the causal mechanism that
controls some environment is unknown to a rational decision-maker. In this way,
action-outcome probabilities can be grounded in causal models in known and
unknown cases. Finally, as an application, we extend the well-known concept of
Nash Equilibrium to the case in which the players of a strategic game consider
causal information.
