{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/causal-fairness-in-action","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/9728d09d-7c2f-4af2-9e3d-c20dc695cc6e","json_url":"https://onlylabs.fyi/signals/9728d09d-7c2f-4af2-9e3d-c20dc695cc6e/signal.json","generated_at":"2026-06-11T03:58:40.441094+00:00","org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/amazon","dossier_json_url":"https://onlylabs.fyi/labs/amazon/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/9728d09d-7c2f-4af2-9e3d-c20dc695cc6e","signal_json":"https://onlylabs.fyi/signals/9728d09d-7c2f-4af2-9e3d-c20dc695cc6e/signal.json","source":"https://github.com/amazon-science/causal-fairness-in-action","lab_dossier":"https://onlylabs.fyi/labs/amazon","lab_dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis":"https://onlylabs.fyi/analysis/amazon","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Amazon (Nova) published amazon-science/causal-fairness-in-action (Jupyter Notebook). 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While statistical fairness metrics are widely used due to their simplicity, they are limited in their ability to explain why disparities occur, as they rely on associative relationships in the data. In contrast, causal fairness metrics aim to uncover the underlying data-generating mechanisms that lead to observed disparities, enabling a deeper understanding of the influence of sensitive attributes and their proxies. Despite their promise, causal fairness metrics have seen limited adoption due to their technical and computational complexity. To address this gap, we present CausalFairnessInAction, the first open-source Python package designed to compute a diverse set of causal fairness metrics at both the group and..."},"evidence_pages":[{"url":"https://github.com/amazon-science/causal-fairness-in-action","final_url":"https://github.com/amazon-science/causal-fairness-in-action","title":"amazon-science/causal-fairness-in-action repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T03:58:40.441094+00:00","bytes":19670,"raw_path":"b3a9c83116deff1144ac3bb96bd3b30cc5ce8b751ba965dd9545db084c40d05a.json","content_hash":"f78505d60d143ae151fac71cb2ca119f66473ebc1ab962fe712259a33a688f41","excerpt_chars":1200,"truncated":true,"excerpt":"amazon-science/causal-fairness-in-action Language: Jupyter Notebook License: NOASSERTION Stars: 6 Forks: 0 Open issues: 1 Created: 2025-11-25T17:21:23Z Pushed: 2026-05-07T06:57:08Z Default branch: main Fork: no Archived: no README: CausalFairnessInAction: An Open Source Python Library for Causal Fairness Analaysis As machine learning (ML) systems are increasingly deployed in high-stakes domains, the need for robust methods to assess fairness has become more critical. While statistical fairness metrics are widely used due to their simplicity, they are limited in their ability to explain why disparities occur, as they rely on associative relationships in the data. In contrast, causal fairness metrics aim to uncover the underlying data-generating mechanisms that lead to observed disparities, enabling a deeper understanding of the influence of sensitive attributes and their proxies. Despite their promise, causal fairness metrics have seen limited adoption due to their technical and computational complexity. 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