{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2XS7TNW7YP66RTU7NJQR34YPTD","short_pith_number":"pith:2XS7TNW7","schema_version":"1.0","canonical_sha256":"d5e5f9b6dfc3fde8ce9f6a611df30f98f6502e8e1790b28dfca411375d8a9aa3","source":{"kind":"arxiv","id":"2305.19802","version":1},"attestation_state":"computed","paper":{"title":"Neuro-Causal Factor Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alex Markham, Bryon Aragam, Liam Solus, Mingyu Liu","submitted_at":"2023-05-31T12:41:20Z","abstract_excerpt":"Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences. We revisit this classic method from the comparatively new perspective given by advancements in causal discovery and deep learning, introducing a framework for Neuro-Causal Factor Analysis (NCFA). Our approach is fully nonparametric: it identifies factors via latent causal discovery methods and then uses a variational autoencod"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.19802","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2023-05-31T12:41:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b2d85eab7d7105fc470705457d3e1fa0f1d62617d443635827dd2da558bbdfd0","abstract_canon_sha256":"8e37a8c00ba4ccf8e24a1fd58c2c250c4bfbcffecfb10752a11d629622dac83d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:16:03.396451Z","signature_b64":"HQXR9ZvqVEwwBI94hp+qmTSlm976y7c2OaLNzs9aYK0cGfytD8+XEuzhyR3NnLqBnnpaHiNbj4DZeS8Dk/yBAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5e5f9b6dfc3fde8ce9f6a611df30f98f6502e8e1790b28dfca411375d8a9aa3","last_reissued_at":"2026-07-05T06:16:03.395861Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:16:03.395861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neuro-Causal Factor Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alex Markham, Bryon Aragam, Liam Solus, Mingyu Liu","submitted_at":"2023-05-31T12:41:20Z","abstract_excerpt":"Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences. We revisit this classic method from the comparatively new perspective given by advancements in causal discovery and deep learning, introducing a framework for Neuro-Causal Factor Analysis (NCFA). Our approach is fully nonparametric: it identifies factors via latent causal discovery methods and then uses a variational autoencod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.19802","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.19802/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.19802","created_at":"2026-07-05T06:16:03.395930+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.19802v1","created_at":"2026-07-05T06:16:03.395930+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.19802","created_at":"2026-07-05T06:16:03.395930+00:00"},{"alias_kind":"pith_short_12","alias_value":"2XS7TNW7YP66","created_at":"2026-07-05T06:16:03.395930+00:00"},{"alias_kind":"pith_short_16","alias_value":"2XS7TNW7YP66RTU7","created_at":"2026-07-05T06:16:03.395930+00:00"},{"alias_kind":"pith_short_8","alias_value":"2XS7TNW7","created_at":"2026-07-05T06:16:03.395930+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.28096","citing_title":"Succinct Graph Representations and Algorithmic Applications","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD","json":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD.json","graph_json":"https://pith.science/api/pith-number/2XS7TNW7YP66RTU7NJQR34YPTD/graph.json","events_json":"https://pith.science/api/pith-number/2XS7TNW7YP66RTU7NJQR34YPTD/events.json","paper":"https://pith.science/paper/2XS7TNW7"},"agent_actions":{"view_html":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD","download_json":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD.json","view_paper":"https://pith.science/paper/2XS7TNW7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.19802&json=true","fetch_graph":"https://pith.science/api/pith-number/2XS7TNW7YP66RTU7NJQR34YPTD/graph.json","fetch_events":"https://pith.science/api/pith-number/2XS7TNW7YP66RTU7NJQR34YPTD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD/action/storage_attestation","attest_author":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD/action/author_attestation","sign_citation":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD/action/citation_signature","submit_replication":"https://pith.science/pith/2XS7TNW7YP66RTU7NJQR34YPTD/action/replication_record"}},"created_at":"2026-07-05T06:16:03.395930+00:00","updated_at":"2026-07-05T06:16:03.395930+00:00"}