You could argue that reinforcement learning policies are already causal models insofar as they relate state-action pairs to the rewards and penalties that they lead to. The trial and error that RL performs in a simulation is an exploration of counterfactuals to establish cause.

But the policies lack introspection. One of the most powerful things we could do is somehow extract causal models from those policies, to see what they learned that lead them to behave more intelligently. That would increase both our knowledge as well as our trust in applying RL.