{"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/quantifying-consistency-and-information-loss","title":"Quantifying Consistency and Information Loss for Causal Abstraction Learning","arxiv_id":"2305.04357","date":"2023-05-07","proceeding":null,"authors":["Fabio Massimo Zennaro","Paolo Turrini","Theodoros Damoulas"],"abstract":"Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstraction, whereby relations may be coarsened and refined according to the need of a modeller. 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