There are a lot of efforts in developing models that understand causal relationships within mainstream machine learning community. Mostly to train models that don't require a lot of training examples. Deep learning usually requires a lot of data and trained models are not easily transferable to other tasks. Yet humans tend to transfer their knowledge from other tasks pretty easily to seemingly unrelated tasks. This seems to be due to our mental models surrounding causal relationships. One example of such efforts is schema networks. It is a model-based approach to RL that exhibits some of the strong generalization abilities that can be key to human-like general intelligence. https://www.vicarious.com/2017/08/07/general-game-playing-wi...