{"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/domain-adaptation-by-using-causal-inference","title":"Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions","arxiv_id":"1707.06422","date":"2017-07-20","proceeding":"NeurIPS 2018 12","authors":["Sara Magliacane","Thijs van Ommen","Tom Claassen","Stephan Bongers","Philip Versteeg","Joris M. Mooij"],"abstract":"An important goal common to domain adaptation and causal inference is to make\naccurate predictions when the distributions for the source (or training)\ndomain(s) and target (or test) domain(s) differ. In many cases, these different\ndistributions can be modeled as different contexts of a single underlying\nsystem, in which each distribution corresponds to a different perturbation of\nthe system, or in causal terms, an intervention. We focus on a class of such\ncausal domain adaptation problems, where data for one or more source domains\nare given, and the task is to predict the distribution of a certain target\nvariable from measurements of other variables in one or more target domains. We\npropose an approach for solving these problems that exploits causal inference\nand does not rely on prior knowledge of the causal graph, the type of\ninterventions or the intervention targets. We demonstrate our approach by\nevaluating a possible implementation on simulated and real world data.","url_abs":"http://arxiv.org/abs/1707.06422v3","url_pdf":"http://arxiv.org/pdf/1707.06422v3.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":"domain-adaptation-by-using-causal-inference","repo_url":"https://github.com/caus-am/dom_adapt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[{"method_slug":"causal-inference","method_name":"Causal inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.06422","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}