{"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/estimating-and-controlling-the-false","title":"Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values","arxiv_id":"1607.03975","date":"2016-07-14","proceeding":null,"authors":["Eric V. Strobl","Peter L. Spirtes","Shyam Visweswaran"],"abstract":"The PC algorithm allows investigators to estimate a complete partially\ndirected acyclic graph (CPDAG) from a finite dataset, but few groups have\ninvestigated strategies for estimating and controlling the false discovery rate\n(FDR) of the edges in the CPDAG. In this paper, we introduce PC with p-values\n(PC-p), a fast algorithm which robustly computes edge-specific p-values and\nthen estimates and controls the FDR across the edges. PC-p specifically uses\nthe p-values returned by many conditional independence tests to upper bound the\np-values of more complex edge-specific hypothesis tests. The algorithm then\nestimates and controls the FDR using the bounded p-values and the\nBenjamini-Yekutieli FDR procedure. Modifications to the original PC algorithm\nalso help PC-p accurately compute the upper bounds despite non-zero Type II\nerror rates. Experiments show that PC-p yields more accurate FDR estimation and\ncontrol across the edges in a variety of CPDAGs compared to alternative\nmethods.","url_abs":"http://arxiv.org/abs/1607.03975v2","url_pdf":"http://arxiv.org/pdf/1607.03975v2.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":"estimating-and-controlling-the-false","repo_url":"https://github.com/ericstrobl/PCp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}