Cluster-Dags as Powerful Background Knowledge For Causal Discovery

TMLR Paper7204 Authors

Abstract: Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. However, current methods face several challenges, especially when dealing with high-dimensional data and complex dependencies. Incorporating prior knowledge about the system can aid causal discovery. In this work, we leverage Cluster-DAGs as a prior knowledge framework to warm-start causal discovery. We show that Cluster-DAGs offer greater flexibility than existing approaches based on tiered background knowledge and introduce two modified constraint-based algorithms, Cluster-PC and Cluster-FCI, for causal discovery in the fully and partially observed setting, respectively. Empirical evaluation on simulated data demonstrates that Cluster-PC and Cluster-FCI outperform their respective baselines without prior knowledge.

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

Previous TMLR Submission Url: https://openreview.net/forum?id=gkQ9xpfzQY&nesting=2&sort=date-desc

Changes Since Last Submission: Corrected the fonts. The submission was desk rejected because we had made an accidental change to the default font without realizing it. This is now corrected.

Assigned Action Editor: ~Yan_Liu1

Submission Number: 7204