{"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/dependence-versus-conditional-dependence-in","title":"Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data","arxiv_id":"1407.7566","date":"2014-07-28","proceeding":null,"authors":["Eric V. Strobl","Shyam Visweswaran"],"abstract":"Motivation: Algorithms that discover variables which are causally related to\na target may inform the design of experiments. With observational gene\nexpression data, many methods discover causal variables by measuring each\nvariable's degree of statistical dependence with the target using dependence\nmeasures (DMs). However, other methods measure each variable's ability to\nexplain the statistical dependence between the target and the remaining\nvariables in the data using conditional dependence measures (CDMs), since this\nstrategy is guaranteed to find the target's direct causes, direct effects, and\ndirect causes of the direct effects in the infinite sample limit. In this\npaper, we design a new algorithm in order to systematically compare the\nrelative abilities of DMs and CDMs in discovering causal variables from gene\nexpression data.\n  Results: The proposed algorithm using a CDM is sample efficient, since it\nconsistently outperforms other state-of-the-art local causal discovery\nalgorithms when samples sizes are small. However, the proposed algorithm using\na CDM outperforms the proposed algorithm using a DM only when sample sizes are\nabove several hundred. These results suggest that accurate causal discovery\nfrom gene expression data using current CDM-based algorithms requires datasets\nwith at least several hundred samples.\n  Availability: The proposed algorithm is freely available at\nhttps://github.com/ericstrobl/DvCD.","url_abs":"http://arxiv.org/abs/1407.7566v1","url_pdf":"http://arxiv.org/pdf/1407.7566v1.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":"dependence-versus-conditional-dependence-in","repo_url":"https://github.com/ericstrobl/DvCD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"}],"methods":[],"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}