DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks. (arXiv:2204.00387v1 [cs.LG])

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 …

arxiv dag generative adversarial networks learning networks