{"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/the-blessings-of-multiple-causes","title":"The Blessings of Multiple Causes","arxiv_id":"1805.06826","date":"2018-05-17","proceeding":null,"authors":["Yixin Wang","David M. Blei"],"abstract":"Causal inference from observational data often assumes \"ignorability,\" that\nall confounders are observed. This assumption is standard yet untestable.\nHowever, many scientific studies involve multiple causes, different variables\nwhose effects are simultaneously of interest. We propose the deconfounder, an\nalgorithm that combines unsupervised machine learning and predictive model\nchecking to perform causal inference in multiple-cause settings. The\ndeconfounder infers a latent variable as a substitute for unobserved\nconfounders and then uses that substitute to perform causal inference. We\ndevelop theory for the deconfounder, and show that it requires weaker\nassumptions than classical causal inference. We analyze its performance in\nthree types of studies: semi-simulated data around smoking and lung cancer,\nsemi-simulated data around genome-wide association studies, and a real dataset\nabout actors and movie revenue. The deconfounder provides a checkable approach\nto estimating closer-to-truth causal effects.","url_abs":"http://arxiv.org/abs/1805.06826v3","url_pdf":"http://arxiv.org/pdf/1805.06826v3.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":"the-blessings-of-multiple-causes","repo_url":"https://github.com/blei-lab/deconfounder_tutorial","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-blessings-of-multiple-causes","repo_url":"https://github.com/rajat641/CSE-472-Causality-Study","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[{"method_slug":"causal-inference","method_name":"Causal inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.06826","atlas_url":"https://app.syntology.ai/?focus=1805.06826","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}