Causal Decision Theory for The Probabilistically Blindfolded

If you can’t or don’t want to ascribe probabilities to the consequences of your actions, classic causal decision theory won’t let you reap the undeniable benefits of causal reasoning for decision making. The following theory fixes this problem. I explain why it’s good to have a causal decision theory that applies to non-deterministic yet non-probabilistic decision problems. I then introduce the underdeterministic framework and subsequently use it to formulate underdeterministic decision theory. The theory applies to decisions with infinitely many possible consequences and to agents who can’t decide on a single causal model representing the decision problem.