  Discovering causal structures from data is a challenging inference problem of
fundamental importance in all areas of science. The appealing properties of
neural networks have recently led to a surge of interest in differentiable
neural network-based methods for learning causal structures from data. So far,
differentiable causal discovery has focused on static datasets of observational
or fixed interventional origin. In this work, we introduce an active
intervention targeting (AIT) method which enables a quick identification of the
underlying causal structure of the data-generating process. Our method
significantly reduces the required number of interactions compared with random
intervention targeting and is applicable for both discrete and continuous
optimization formulations of learning the underlying directed acyclic graph
(DAG) from data. We examine the proposed method across multiple frameworks in a
wide range of settings and demonstrate superior performance on multiple
benchmarks from simulated to real-world data.
