{"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/split-door-criterion-identification-of-causal","title":"Split-door criterion: Identification of causal effects through auxiliary outcomes","arxiv_id":"1611.09414","date":"2016-11-28","proceeding":null,"authors":["Amit Sharma","Jake M. Hofman","Duncan J. Watts"],"abstract":"We present a method for estimating causal effects in time series data when\nfine-grained information about the outcome of interest is available.\nSpecifically, we examine what we call the split-door setting, where the outcome\nvariable can be split into two parts: one that is potentially affected by the\ncause being studied and another that is independent of it, with both parts\nsharing the same (unobserved) confounders. We show that under these conditions,\nthe problem of identification reduces to that of testing for independence among\nobserved variables, and present a method that uses this approach to\nautomatically find subsets of the data that are causally identified. We\ndemonstrate the method by estimating the causal impact of Amazon's recommender\nsystem on traffic to product pages, finding thousands of examples within the\ndataset that satisfy the split-door criterion. Unlike past studies based on\nnatural experiments that were limited to a single product category, our method\napplies to a large and representative sample of products viewed on the site. In\nline with previous work, we find that the widely-used click-through rate (CTR)\nmetric overestimates the causal impact of recommender systems; depending on the\nproduct category, we estimate that 50-80\\% of the traffic attributed to\nrecommender systems would have happened even without any recommendations. We\nconclude with guidelines for using the split-door criterion as well as a\ndiscussion of other contexts where the method can be applied.","url_abs":"http://arxiv.org/abs/1611.09414v2","url_pdf":"http://arxiv.org/pdf/1611.09414v2.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":"split-door-criterion-identification-of-causal","repo_url":"https://github.com/amit-sharma/splitdoor-causal-criterion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"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}