  Causal discovery is a major task with the utmost importance for machine
learning since causal structures can enable models to go beyond pure
correlation-based inference and significantly boost their performance. However,
finding causal structures from data poses a significant challenge both in
computational effort and accuracy, let alone its impossibility without
interventions in general. In this paper, we develop a meta-reinforcement
learning algorithm that performs causal discovery by learning to perform
interventions such that it can construct an explicit causal graph. Apart from
being useful for possible downstream applications, the estimated causal graph
also provides an explanation for the data-generating process. In this article,
we show that our algorithm estimates a good graph compared to the SOTA
approaches, even in environments whose underlying causal structure is
previously unseen. Further, we make an ablation study that shows how learning
interventions contribute to the overall performance of our approach. We
conclude that interventions indeed help boost the performance, efficiently
yielding an accurate estimate of the causal structure of a possibly unseen
environment.
