Learning to Induce Causal Structure

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. performs the worst, which is to be expect, since intervention data is need for causal identifiability. The performance of our model improves as it observes more interventions, this suggest that our model is able to extract useful information from interventions in order to predict the causal structure.