{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UR7CGMXV7WTLHZ4GZH6DDBPDPE","short_pith_number":"pith:UR7CGMXV","schema_version":"1.0","canonical_sha256":"a47e2332f5fda6b3e786c9fc3185e37915a808c0d5a9eb50ca9928edddf802bb","source":{"kind":"arxiv","id":"2411.14665","version":1},"attestation_state":"computed","paper":{"title":"Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.CO"],"primary_cat":"stat.ML","authors_text":"Han Yu, Lynda Aouar","submitted_at":"2024-11-22T01:54:53Z","abstract_excerpt":"Adaptive causal representation learning from observational data is presented, integrated with an efficient sample splitting technique within the semiparametric estimating equation framework. The support points sample splitting (SPSS), a subsampling method based on energy distance, is employed for efficient double machine learning (DML) in causal inference. The support points are selected and split as optimal representative points of the full raw data in a random sample, in contrast to the traditional random splitting, and providing an optimal sub-representation of the underlying data generatin"},"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":"2411.14665","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-11-22T01:54:53Z","cross_cats_sorted":["cs.LG","stat.AP","stat.CO"],"title_canon_sha256":"6e971342c6ee28becbb6a2792936be7511949fdebef634f231c5b5b5f65c51e8","abstract_canon_sha256":"4227ee2dd4f3537eaadaff41f0a68c69da2901e11aa2a0e13e6b4fd1a0787ba0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:55.114499Z","signature_b64":"XW1XFex8NAtkD+HTZMAz9bHRV2KjVCUN7vfaX7i/OFwwEFNBZtG4FIwpGPyxLxUcCSJ+EWJNoYHRIEWXhzAYAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a47e2332f5fda6b3e786c9fc3185e37915a808c0d5a9eb50ca9928edddf802bb","last_reissued_at":"2026-07-05T09:38:55.113983Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:55.113983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.CO"],"primary_cat":"stat.ML","authors_text":"Han Yu, Lynda Aouar","submitted_at":"2024-11-22T01:54:53Z","abstract_excerpt":"Adaptive causal representation learning from observational data is presented, integrated with an efficient sample splitting technique within the semiparametric estimating equation framework. The support points sample splitting (SPSS), a subsampling method based on energy distance, is employed for efficient double machine learning (DML) in causal inference. The support points are selected and split as optimal representative points of the full raw data in a random sample, in contrast to the traditional random splitting, and providing an optimal sub-representation of the underlying data generatin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14665","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/2411.14665/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":"2411.14665","created_at":"2026-07-05T09:38:55.114046+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14665v1","created_at":"2026-07-05T09:38:55.114046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14665","created_at":"2026-07-05T09:38:55.114046+00:00"},{"alias_kind":"pith_short_12","alias_value":"UR7CGMXV7WTL","created_at":"2026-07-05T09:38:55.114046+00:00"},{"alias_kind":"pith_short_16","alias_value":"UR7CGMXV7WTLHZ4G","created_at":"2026-07-05T09:38:55.114046+00:00"},{"alias_kind":"pith_short_8","alias_value":"UR7CGMXV","created_at":"2026-07-05T09:38:55.114046+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/UR7CGMXV7WTLHZ4GZH6DDBPDPE","json":"https://pith.science/pith/UR7CGMXV7WTLHZ4GZH6DDBPDPE.json","graph_json":"https://pith.science/api/pith-number/UR7CGMXV7WTLHZ4GZH6DDBPDPE/graph.json","events_json":"https://pith.science/api/pith-number/UR7CGMXV7WTLHZ4GZH6DDBPDPE/events.json","paper":"https://pith.science/paper/UR7CGMXV"},"agent_actions":{"view_html":"https://pith.science/pith/UR7CGMXV7WTLHZ4GZH6DDBPDPE","download_json":"https://pith.science/pith/UR7CGMXV7WTLHZ4GZH6DDBPDPE.json","view_paper":"https://pith.science/paper/UR7CGMXV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14665&json=true","fetch_graph":"https://pith.science/api/pith-number/UR7CGMXV7WTLHZ4GZH6DDBPDPE/graph.json","fetch_events":"https://pith.science/api/pith-number/UR7CGMXV7WTLHZ4GZH6DDBPDPE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UR7CGMXV7WTLHZ4GZH6DDBPDPE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UR7CGMXV7WTLHZ4GZH6DDBPDPE/action/storage_attestation","attest_author":"https://pith.science/pith/UR7CGMXV7WTLHZ4GZH6DDBPDPE/action/author_attestation","sign_citation":"https://pith.science/pith/UR7CGMXV7WTLHZ4GZH6DDBPDPE/action/citation_signature","submit_replication":"https://pith.science/pith/UR7CGMXV7WTLHZ4GZH6DDBPDPE/action/replication_record"}},"created_at":"2026-07-05T09:38:55.114046+00:00","updated_at":"2026-07-05T09:38:55.114046+00:00"}