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Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

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Bibliographic Details
Published in:Journal of Machine Learning Research
Format: Online Article RSS Article
Published: 2026
Subjects:
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container_title Journal of Machine Learning Research
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:4963
institution FRELIP
journal_source_facet Journal of Machine Learning Research
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
title_auth Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
title_full Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
title_fullStr Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
title_full_unstemmed Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
title_short Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
title_sort causal abstraction: a theoretical foundation for mechanistic interpretability
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://jmlr.org/papers/v26/23-0058.html