CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement
  Learning
Andreas W. M. Sauter Nicolò Botteghi Erman Acar Aske Plaat
Causal discovery is the challenging task of inferring causal structure from
data. Motivated by Pearl's Causal Hierarchy (PCH), which tells us that passive
observations alone are not enough to distinguish correlation from causation,
there has been a recent push to incorporate interventions into machine learning
research. Reinforcement learning provides a convenient framework for such an
active approach to learning. This paper presents CORE, a deep reinforcement
learning-based approach for causal discovery and intervention planning. CORE
learns to sequentially reconstruct causal graphs from data while learning to
perform informative interventions. Our results demonstrate that CORE
generalizes to unseen graphs and efficiently uncovers causal structures.
Furthermore, CORE scales to larger graphs with up to 10 variables and
outperforms existing approaches in structure estimation accuracy and sample
efficiency. All relevant code and supplementary material can be found at
https://github.com/sa-and/CORE