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.
Yep, that paper has been on my list for a while, but I have thus far been unable to penetrate the formalisms that the Causal Incentive Group uses. This paper in particular also seems have some fairly limiting assumptions in the theorem.
You may be interested in this if you haven’t seen it already: Robust Agents Learn Causal World Models (DM):
h/t Gwern
Yep, that paper has been on my list for a while, but I have thus far been unable to penetrate the formalisms that the Causal Incentive Group uses. This paper in particular also seems have some fairly limiting assumptions in the theorem.