Gradient-Based Neural DAG Learning with Interventions

Abstract

Decision making based on statistical association alone can be a dangerous en- deavor due to non-causal associations. Ideally, one would rely on causal rela- tionships that enable reasoning about the effect of interventions. Several methods have been proposed to discover such relationships from observational and inter- ventional data. Among them, GraN-DAG, a method that relies on the constrained optimization of neural networks, was shown to produce state-of-the-art results among algorithms relying purely on observational data. However, it is limited to observational data and cannot make use of interventions. In this work, we extend GraN-DAG to support interventional data and show that this improves its ability to infer causal structures.