{"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/ancestral-causal-inference","title":"Ancestral Causal Inference","arxiv_id":"1606.07035","date":"2016-06-22","proceeding":"NeurIPS 2016 12","authors":["Sara Magliacane","Tom Claassen","Joris M. Mooij"],"abstract":"Constraint-based causal discovery from limited data is a notoriously\ndifficult challenge due to the many borderline independence test decisions.\nSeveral approaches to improve the reliability of the predictions by exploiting\nredundancy in the independence information have been proposed recently. Though\npromising, existing approaches can still be greatly improved in terms of\naccuracy and scalability. We present a novel method that reduces the\ncombinatorial explosion of the search space by using a more coarse-grained\nrepresentation of causal information, drastically reducing computation time.\nAdditionally, we propose a method to score causal predictions based on their\nconfidence. Crucially, our implementation also allows one to easily combine\nobservational and interventional data and to incorporate various types of\navailable background knowledge. We prove soundness and asymptotic consistency\nof our method and demonstrate that it can outperform the state-of-the-art on\nsynthetic data, achieving a speedup of several orders of magnitude. We\nillustrate its practical feasibility by applying it on a challenging protein\ndata set.","url_abs":"http://arxiv.org/abs/1606.07035v3","url_pdf":"http://arxiv.org/pdf/1606.07035v3.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":"ancestral-causal-inference","repo_url":"https://github.com/caus-am/aci","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"},{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.07035","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}