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.

翻译：长期以来，因果推理在鲁棒且通用智能中的基础作用一直受到假设，但尚不明确智能体是否必须学习因果模型才能泛化到新领域，抑或其他归纳偏置已足够。我们回答了这一问题，证明任何能在大规模分布偏移下满足遗憾上界的智能体，都必须学习数据生成过程的近似因果模型——对于最优智能体而言，该模型将收敛至真实的因果模型。我们讨论了这一结果对迁移学习、因果推断等多个研究领域的启示。