DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks
This addresses the problem of causal discovery for researchers and practitioners, though it appears incremental as it adapts existing techniques to this domain.
The paper tackles the challenge of learning causal structures from data by proposing DAG-WGAN, which combines Wasserstein GANs with an acyclicity constraint to discover Directed Acyclic Graphs (DAGs). Experiments show it scales well, handles continuous and discrete data, and demonstrates good performance compared to state-of-the-art methods.
The combinatorial search space presents a significant challenge to learning causality from data. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constraint, allowing for the exploration of deep generative models to better capture data sample distributions and support the discovery of Directed Acyclic Graphs (DAGs) that faithfully represent the underlying data distribution. However, so far no study has investigated the use of Wasserstein distance for causal structure learning via generative models. This paper proposes a new model named DAG-WGAN, which combines the Wasserstein-based adversarial loss, an auto-encoder architecture together with an acyclicity constraint. DAG-WGAN simultaneously learns causal structures and improves its data generation capability by leveraging the strength from the Wasserstein distance metric. Compared with other models, it scales well and handles both continuous and discrete data. Our experiments have evaluated DAG-WGAN against the state-of-the-art and demonstrated its good performance.