  There exist well-developed frameworks for causal modelling, but these require
rather a lot of human domain expertise to define causal variables and perform
interventions. In order to enable autonomous agents to learn abstract causal
models through interactive experience, the existing theoretical foundations
need to be extended and clarified. Existing frameworks give no guidance
regarding variable choice / representation, and more importantly, give no
indication as to which behaviour policies or physical transformations of state
space shall count as interventions. The framework sketched in this paper
describes actions as transformations of state space, for instance induced by an
agent running a policy. This makes it possible to describe in a uniform way
both transformations of the micro-state space and abstract models thereof, and
say when the latter is veridical / grounded / natural. We then introduce
(causal) variables, define a mechanism as an invariant predictor, and say when
an action can be viewed as a ``surgical intervention'', thus bringing the
objective of causal representation \& intervention skill learning into clearer
focus.
