{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/RCAWithMissingStructuralKnowledgeCode","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/ec7a82e6-d03c-4c86-8872-028c2580306b","json_url":"https://onlylabs.fyi/signals/ec7a82e6-d03c-4c86-8872-028c2580306b/signal.json","generated_at":"2026-06-11T03:58:40.576568+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/ec7a82e6-d03c-4c86-8872-028c2580306b","signal_json":"https://onlylabs.fyi/signals/ec7a82e6-d03c-4c86-8872-028c2580306b/signal.json","source":"https://github.com/amazon-science/RCAWithMissingStructuralKnowledgeCode","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/RCAWithMissingStructuralKnowledgeCode (Jupyter Notebook). 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Accepted NeurIPS 2025.` It includes experiment runners, evaluation utilities, result saving, and plotting scripts for comparing algorithms under different experimental setups. --- 📌 Features - **Synthetic data generation** via random SCM generation followed by anomaly injection. - **Multiple experiment modes**: - `vary_graph_size`: Fix anomaly strength, vary graph size. - `vary_anomaly_strength`: Fix graph size, vary anomaly strength. - `pro_rca`: Run domain-specific RCA experiments - `sock_shop`: Run methods on semi-synthetic Sock-shop 2 dataset (Pham et al. 2024, Root Cause Analysis for Microservice System based on Causal Inference: How Far..."},"evidence_pages":[{"url":"https://github.com/amazon-science/RCAWithMissingStructuralKnowledgeCode","final_url":"https://github.com/amazon-science/RCAWithMissingStructuralKnowledgeCode","title":"amazon-science/RCAWithMissingStructuralKnowledgeCode repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T03:58:40.576568+00:00","bytes":23312,"raw_path":"f624b4427a5b99b2302887e167c6171046ff10dc2937a02a9b1bc270608b3491.json","content_hash":"7decdfbb7ae4b450cf38bc2b197a913f62b364b5592dda4dc8abfd2ae11a72df","excerpt_chars":1200,"truncated":true,"excerpt":"amazon-science/RCAWithMissingStructuralKnowledgeCode Language: Jupyter Notebook License: Apache-2.0 Stars: 8 Forks: 2 Open issues: 1 Created: 2025-11-20T17:26:32Z Pushed: 2026-03-26T14:03:45Z Default branch: main Fork: no Archived: no README: Root Cause Analysis of Outliers with Missing Structural Knowledge in Python This project includes the implementations of the experiments in the paper `Orchard, W. R.*, Okati, N.*, Garrido Mejia, S.H., Blöbaum, P. and Janzing, D. (2025) Root Cause Analysis of Outliers with Missing Structural Knowledge. Accepted NeurIPS 2025.` It includes experiment runners, evaluation utilities, result saving, and plotting scripts for comparing algorithms under different experimental setups. --- 📌 Features - **Synthetic data generation** via random SCM generation followed by anomaly injection. - **Multiple experiment modes**: - `vary_graph_size`: Fix anomaly strength, vary graph size. - `vary_anomaly_strength`: Fix graph size, vary anomaly strength. - `pro_rca`: Run domain-specific RCA experiments - `sock_shop`: Run methods on semi-synthetic Sock-shop 2 dataset (Pham et al. 2024, Root Cause Analysis for Microservice System based on Causal Inference: How Far..."}],"related_signals":[{"id":"087c32a2-6ad0-4981-9315-11fdd32a0153","url":"https://onlylabs.fyi/signals/087c32a2-6ad0-4981-9315-11fdd32a0153","source_url":"https://github.com/amazon-science/reskill","title":"amazon-science/reskill","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-06-04T02:13:35+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e5701aed-6cd3-48dd-bfa6-ef839031e2e8","url":"https://onlylabs.fyi/signals/e5701aed-6cd3-48dd-bfa6-ef839031e2e8","source_url":"https://github.com/amazon-science/dualkv-flash-attn-for-rl","title":"amazon-science/dualkv-flash-attn-for-rl","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-27T17:38:58+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"8af28f0c-7331-4b08-b517-e18b3555e503","url":"https://onlylabs.fyi/signals/8af28f0c-7331-4b08-b517-e18b3555e503","source_url":"https://github.com/amazon-science/EvoMAS","title":"amazon-science/EvoMAS","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-19T19:23:29+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e3ff8718-7daa-4ebd-a3e6-3d825c538b74","url":"https://onlylabs.fyi/signals/e3ff8718-7daa-4ebd-a3e6-3d825c538b74","source_url":"https://github.com/amazon-science/adaptive-layerwise-perturbation","title":"amazon-science/adaptive-layerwise-perturbation","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-14T17:44:17+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"9afcd328-0124-485c-8ace-9c3ad546e316","url":"https://onlylabs.fyi/signals/9afcd328-0124-485c-8ace-9c3ad546e316","source_url":"https://github.com/amazon-science/temporal-reasoning-dataset","title":"amazon-science/temporal-reasoning-dataset","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-13T13:07:08+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e19ce80b-3d6a-4aaf-9b1a-82d1b19ab682","url":"https://onlylabs.fyi/signals/e19ce80b-3d6a-4aaf-9b1a-82d1b19ab682","source_url":"https://github.com/amazon-science/PROF-GRPO","title":"amazon-science/PROF-GRPO","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-12T19:43:55+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"}]}