  Interventions are central to causal learning and reasoning. Yet ultimately an
intervention is an abstraction: an agent embedded in a physical environment
(perhaps modeled as a Markov decision process) does not typically come equipped
with the notion of an intervention -- its action space is typically
ego-centric, without actions of the form `intervene on X'. Such a
correspondence between ego-centric actions and interventions would be
challenging to hard-code. It would instead be better if an agent learnt which
sequence of actions allow it to make targeted manipulations of the environment,
and learnt corresponding representations that permitted learning from
observation. Here we show how a meta-learning approach can be used to perform
causal learning in this challenging setting, where the action-space is not a
set of interventions and the observation space is a high-dimensional space with
a latent causal structure. A meta-reinforcement learning algorithm is used to
learn relationships that transfer on observational causal learning tasks. This
work shows how advances in deep reinforcement learning and meta-learning can
provide intervention-centric causal learning in high-dimensional environments
with a latent causal structure.
