  Complex adaptive agents consistently achieve their goals by solving problems
that seem to require an understanding of causal information, information
pertaining to the causal relationships that exist among elements of combined
agent-environment systems. Causal cognition studies and describes the main
characteristics of causal learning and reasoning in human and non-human
animals, offering a conceptual framework to discuss cognitive performances
based on the level of apparent causal understanding of a task. Despite the use
of formal intervention-based models of causality, including causal Bayesian
networks, psychological and behavioural research on causal cognition does not
yet offer a computational account that operationalises how agents acquire a
causal understanding of the world. Machine and reinforcement learning research
on causality, especially involving disentanglement as a candidate process to
build causal representations, represent on the one hand a concrete attempt at
designing causal artificial agents that can shed light on the inner workings of
natural causal cognition. In this work, we connect these two areas of research
to build a unifying framework for causal cognition that will offer a
computational perspective on studies of animal cognition, and provide insights
in the development of new algorithms for causal reinforcement learning in AI.
