Learning Directed Acyclic Graphs from Partial Orderings

arXiv:2403.16031v1 Announce Type: cross
Abstract: Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistically challenging. Moreover, without additional information, the direction of edges may not be estimable from observational data. In contrast, given a complete causal ordering of the variables, the problem can be solved efficiently, even in high dimensions. In this paper, we consider the intermediate problem of learning DAGs when a partial …

abstract arxiv causal contrast cs.lg dag data general graphs information random relationships stat.me stat.ml type variables