{"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/federated-causal-discovery-from-heterogeneous","title":"Federated Causal Discovery from Heterogeneous Data","arxiv_id":"2402.13241","date":"2024-02-20","proceeding":null,"authors":["Loka Li","Ignavier Ng","Gongxu Luo","Biwei Huang","Guangyi Chen","Tongliang Liu","Bin Gu","Kun Zhang"],"abstract":"Conventional causal discovery methods rely on centralized data, which is inconsistent with the decentralized nature of data in many real-world situations. This discrepancy has motivated the development of federated causal discovery (FCD) approaches. 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