  It has long been hypothesised that causal reasoning plays a fundamental role
in robust and general intelligence. However, it is not known if agents must
learn causal models in order to generalise to new domains, or if other
inductive biases are sufficient. We answer this question, showing that any
agent capable of satisfying a regret bound under a large set of distributional
shifts must have learned an approximate causal model of the data generating
process, which converges to the true causal model for optimal agents. We
discuss the implications of this result for several research areas including
transfer learning and causal inference.
