Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models

arXiv:2404.12592v1 Announce Type: cross
Abstract: We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: i) they cannot provide optimality guarantees and can suffer from learning sub-optimal models; ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We overcome these shortcomings and …

abstract art arxiv continuous data equation generated graphs least linear programming state stat.me stat.ml study type