  The fundamental challenge in causal induction is to infer the underlying
graph structure given observational and/or interventional data. Most existing
causal induction algorithms operate by generating candidate graphs and
evaluating them using either score-based methods (including continuous
optimization) or independence tests. In our work, we instead treat the
inference process as a black box and design a neural network architecture that
learns the mapping from both observational and interventional data to graph
structures via supervised training on synthetic graphs. The learned model
generalizes to new synthetic graphs, is robust to train-test distribution
shifts, and achieves state-of-the-art performance on naturalistic graphs for
low sample complexity.
