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Decision-making under uncertainty and causal thinking are fundamental aspectsof intelligent reasoning. Decision-making has been well studied when theavailable information is considered at the associative (probabilistic) level.The classical Theorems of von Neumann-Morgenstern and Savage provide a formalcriterion for rational choice using associative information: maximize expectedutility. There is an ongoing debate around the origin of probabilities involvedin such calculation. In this work, we will show how the probabilities fordecision-making can be grounded in causal models by considering decisionproblems in which the available actions and consequences are causallyconnected. In this setting, actions are regarded as an intervention over acausal model. Then, we extend a previous causal decision-making result, whichrelies on a known causal model, to the case in which the causal mechanism thatcontrols some environment is unknown to a rational decision-maker. In this way,action-outcome probabilities can be grounded in causal models in known andunknown cases. Finally, as an application, we extend the well-known concept ofNash Equilibrium to the case in which the players of a strategic game considercausal information.

Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models