Gradient-Based Neural DAG Learning
This work addresses causal discovery for researchers and practitioners by offering a scalable method for learning DAGs with complex interactions, though it is incremental as it builds on existing continuous optimization formulations.
The authors tackled the problem of learning directed acyclic graphs (DAGs) from observational data by proposing a gradient-based neural approach that extends continuous optimization to handle nonlinear relationships, outperforming current continuous methods on most tasks and being competitive with greedy search methods on causal inference metrics.
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while avoiding the combinatorial nature of the problem. In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods. On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks, while being competitive with existing greedy search methods on important metrics for causal inference.