Expressivity of Parametrized Distributions over DAGs for Causal Discovery

TMLR Paper5012 Authors

Abstract: Bayesian approaches for causal discovery can in principle quantify uncertainty in the prediction of the underlying causal structure, typically modeled by a directed acyclic graph (DAG). Various semi-implicit models for parametrized distributions over DAGs have been proposed, but their limitations have not been studied thoroughly. In this work, we focus on the expressiveness of parametrized distributions over DAGs in the context of causal structure learning and show several limitations of candidate models in a theoretical analysis and validate them in experiments. To overcome them, we propose mixture models of distributions over DAGs.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Bryon_Aragam1

Submission Number: 5012