{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/qwo-speeding-up-permutation-based-causal","title":"QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs","arxiv_id":"2410.23155","date":"2024-10-30","proceeding":null,"authors":["Mohammad ShahverdiKondori","Ehsan Mokhtarian","Negar Kiyavash"],"abstract":"Causal discovery is essential for understanding relationships among variables of interest in many scientific domains. In this paper, we focus on permutation-based methods for learning causal graphs in Linear Gaussian Acyclic Models (LiGAMs), where the permutation encodes a causal ordering of the variables. Existing methods in this setting are not scalable due to their high computational complexity. These methods are comprised of two main components: (i) constructing a specific DAG, $\\mathcal{G}^\\pi$, for a given permutation $\\pi$, which represents the best structure that can be learned from the available data while adhering to $\\pi$, and (ii) searching over the space of permutations (i.e., causal orders) to minimize the number of edges in $\\mathcal{G}^\\pi$. We introduce QWO, a novel approach that significantly enhances the efficiency of computing $\\mathcal{G}^\\pi$ for a given permutation $\\pi$. QWO has a speed-up of $O(n^2)$ ($n$ is the number of variables) compared to the state-of-the-art BIC-based method, making it highly scalable. We show that our method is theoretically sound and can be integrated into existing search strategies such as GRASP and hill-climbing-based methods to improve their performance.","url_abs":"https://arxiv.org/abs/2410.23155v1","url_pdf":"https://arxiv.org/pdf/2410.23155v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"qwo-speeding-up-permutation-based-causal","repo_url":"https://github.com/ban-epfl/QWO","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}