{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2009:CH7BMSDPQDCM7XA3XVKKQ2XJYM","short_pith_number":"pith:CH7BMSDP","schema_version":"1.0","canonical_sha256":"11fe16486f80c4cfdc1bbd54a86ae9c33f5013e8aa98e5140f2590ae6b3ada1a","source":{"kind":"arxiv","id":"0904.0838","version":2},"attestation_state":"computed","paper":{"title":"Finding Exogenous Variables in Data with Many More Variables than Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Aapo Hyvarinen, Seiya Imoto, Shohei Shimizu, Takashi Washio","submitted_at":"2009-04-06T03:36:01Z","abstract_excerpt":"Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations (p<n, p: the number of variables and n: the number of observations). However, modern datasets including gene expression data need high-dimensional causal modeling in challenging situations with orders of magnitude more variables than observations (p>>n). In this paper, we propose a method to find exogenous variables in a linear non-Gaussian causal model, which requires much smaller sample sizes than conventional methods and works even when p>>n. The key idea is t"},"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":"0904.0838","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2009-04-06T03:36:01Z","cross_cats_sorted":[],"title_canon_sha256":"6d524a0d57e5968074739c571cf0d03ddfef5848e165577f403a781be210c47c","abstract_canon_sha256":"ba8b11f9d2efc17c5820c33571291351e20a46df03636cc638e585a35bd5db1e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:24:53.322730Z","signature_b64":"LIc2AUXyDPpgfr8Mo+qbAlD1uJPyeXa7kLrCiuJNHtTh/8jBjzqwzmFG0qVLd5dRC182eMrhyvK2XRO1FOZkAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11fe16486f80c4cfdc1bbd54a86ae9c33f5013e8aa98e5140f2590ae6b3ada1a","last_reissued_at":"2026-05-18T04:24:53.322172Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:24:53.322172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finding Exogenous Variables in Data with Many More Variables than Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Aapo Hyvarinen, Seiya Imoto, Shohei Shimizu, Takashi Washio","submitted_at":"2009-04-06T03:36:01Z","abstract_excerpt":"Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations (p<n, p: the number of variables and n: the number of observations). However, modern datasets including gene expression data need high-dimensional causal modeling in challenging situations with orders of magnitude more variables than observations (p>>n). In this paper, we propose a method to find exogenous variables in a linear non-Gaussian causal model, which requires much smaller sample sizes than conventional methods and works even when p>>n. The key idea is t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0904.0838","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"0904.0838","created_at":"2026-05-18T04:24:53.322255+00:00"},{"alias_kind":"arxiv_version","alias_value":"0904.0838v2","created_at":"2026-05-18T04:24:53.322255+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0904.0838","created_at":"2026-05-18T04:24:53.322255+00:00"},{"alias_kind":"pith_short_12","alias_value":"CH7BMSDPQDCM","created_at":"2026-05-18T12:25:59.703012+00:00"},{"alias_kind":"pith_short_16","alias_value":"CH7BMSDPQDCM7XA3","created_at":"2026-05-18T12:25:59.703012+00:00"},{"alias_kind":"pith_short_8","alias_value":"CH7BMSDP","created_at":"2026-05-18T12:25:59.703012+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM","json":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM.json","graph_json":"https://pith.science/api/pith-number/CH7BMSDPQDCM7XA3XVKKQ2XJYM/graph.json","events_json":"https://pith.science/api/pith-number/CH7BMSDPQDCM7XA3XVKKQ2XJYM/events.json","paper":"https://pith.science/paper/CH7BMSDP"},"agent_actions":{"view_html":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM","download_json":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM.json","view_paper":"https://pith.science/paper/CH7BMSDP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0904.0838&json=true","fetch_graph":"https://pith.science/api/pith-number/CH7BMSDPQDCM7XA3XVKKQ2XJYM/graph.json","fetch_events":"https://pith.science/api/pith-number/CH7BMSDPQDCM7XA3XVKKQ2XJYM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM/action/storage_attestation","attest_author":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM/action/author_attestation","sign_citation":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM/action/citation_signature","submit_replication":"https://pith.science/pith/CH7BMSDPQDCM7XA3XVKKQ2XJYM/action/replication_record"}},"created_at":"2026-05-18T04:24:53.322255+00:00","updated_at":"2026-05-18T04:24:53.322255+00:00"}