{"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/structural-intervention-distance-sid-for","title":"Structural Intervention Distance (SID) for Evaluating Causal Graphs","arxiv_id":"1306.1043","date":"2013-06-05","proceeding":null,"authors":["Jonas Peters","Peter Bühlmann"],"abstract":"Causal inference relies on the structure of a graph, often a directed acyclic\ngraph (DAG). Different graphs may result in different causal inference\nstatements and different intervention distributions. To quantify such\ndifferences, we propose a (pre-) distance between DAGs, the structural\nintervention distance (SID). The SID is based on a graphical criterion only and\nquantifies the closeness between two DAGs in terms of their corresponding\ncausal inference statements. It is therefore well-suited for evaluating graphs\nthat are used for computing interventions. Instead of DAGs it is also possible\nto compare CPDAGs, completed partially directed acyclic graphs that represent\nMarkov equivalence classes. Since it differs significantly from the popular\nStructural Hamming Distance (SHD), the SID constitutes a valuable additional\nmeasure. We discuss properties of this distance and provide an efficient\nimplementation with software code available on the first author's homepage (an\nR package is under construction).","url_abs":"http://arxiv.org/abs/1306.1043v2","url_pdf":"http://arxiv.org/pdf/1306.1043v2.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":"structural-intervention-distance-sid-for","repo_url":"https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"structural-intervention-distance-sid-for","repo_url":"https://github.com/FenTechSolutions/CausalDiscoveryToolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"structural-intervention-distance-sid-for","repo_url":"https://github.com/elementai/causal_discovery_toolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"structural-intervention-distance-sid-for","repo_url":"https://github.com/2023-MindSpore-1/ms-code-18/tree/main/LearningToSeeInTheDark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1306.1043","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}