{"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/identifying-coarse-grained-independent-causal","title":"Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision","arxiv_id":null,"date":"2021-01-01","proceeding":"1st Conference on Causal Learning and Reasoning 2022 2","authors":["Xiaoyang Wang","Klara Nahrstedt","Oluwasanmi O Koyejo"],"abstract":"Current approaches for learning disentangled representations assume that independent latent variables generate the data through a single data generation process. In contrast, this manuscript considers independent causal mechanisms (ICM), which, unlike disentangled representations, directly model multiple data generation processes in a coarse granularity. In this work, we aim to learn a model that isolates each mechanism and approximates the ground-truth ICM from observational data. We outline sufficient conditions under which the ICM can be learned and isolated using a single self-supervised generative model with a mixture prior, simplifying previous methods. Moreover, we implement a generative model with an identifiable structural latent space by combining the ICM with a shared latent space. We compare this ICM approach to disentangled representations on various downstream tasks, showing that the ICM is more robust to intervention, co-variant shift, and noise due to the isolation between the data generation processes.","url_abs":"https://openreview.net/forum?id=fMHwogGqTYs","url_pdf":"https://openreview.net/pdf?id=fMHwogGqTYs","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":"identifying-coarse-grained-independent-causal","repo_url":"https://github.com/Xiaoyang-Wang/ICM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}