{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7QSL6IBFTKQMIZ3OLLNHFHRYOD","short_pith_number":"pith:7QSL6IBF","schema_version":"1.0","canonical_sha256":"fc24bf20259aa0c4676e5ada729e3870fde41c68aae5f5a7f364b8ce1011b5b7","source":{"kind":"arxiv","id":"2406.05504","version":4},"attestation_state":"computed","paper":{"title":"G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Feng Wu, Hong Xiong, Leon Deng, Li-wei H Lehman, Megan Su","submitted_at":"2024-06-08T16:04:33Z","abstract_excerpt":"In the context of medical decision making, counterfactual prediction enables clinicians to predict treatment outcomes of interest under alternative courses of therapeutic actions given observed patient history. In this work, we present G-Transformer for counterfactual outcome prediction under dynamic and time-varying treatment strategies. Our approach leverages a Transformer architecture to capture complex, long-range dependencies in time-varying covariates while enabling g-computation, a causal inference method for estimating the effects of dynamic treatment regimes. Specifically, we use a Tr"},"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":"2406.05504","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-08T16:04:33Z","cross_cats_sorted":[],"title_canon_sha256":"59e677d5d5bc6f22f8a1125af9353a4ae80c56b67f1c652843e18653be806031","abstract_canon_sha256":"ba4267df2d73dbd8a519b3ffce9b24e1341a347887ca660203df1ab41a9fff55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:47.472406Z","signature_b64":"0UtPGjDET278QFoV5MYbntH/AxC/IvcUwcvkggURabTxJ03G+hQ7O9UllalVL+1JCCtxa4MzBoIpniJhkPEFBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc24bf20259aa0c4676e5ada729e3870fde41c68aae5f5a7f364b8ce1011b5b7","last_reissued_at":"2026-07-05T09:11:47.471907Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:47.471907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Feng Wu, Hong Xiong, Leon Deng, Li-wei H Lehman, Megan Su","submitted_at":"2024-06-08T16:04:33Z","abstract_excerpt":"In the context of medical decision making, counterfactual prediction enables clinicians to predict treatment outcomes of interest under alternative courses of therapeutic actions given observed patient history. In this work, we present G-Transformer for counterfactual outcome prediction under dynamic and time-varying treatment strategies. Our approach leverages a Transformer architecture to capture complex, long-range dependencies in time-varying covariates while enabling g-computation, a causal inference method for estimating the effects of dynamic treatment regimes. Specifically, we use a Tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05504","kind":"arxiv","version":4},"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/2406.05504/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":"2406.05504","created_at":"2026-07-05T09:11:47.471969+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05504v4","created_at":"2026-07-05T09:11:47.471969+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05504","created_at":"2026-07-05T09:11:47.471969+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QSL6IBFTKQM","created_at":"2026-07-05T09:11:47.471969+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QSL6IBFTKQMIZ3O","created_at":"2026-07-05T09:11:47.471969+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QSL6IBF","created_at":"2026-07-05T09:11:47.471969+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/7QSL6IBFTKQMIZ3OLLNHFHRYOD","json":"https://pith.science/pith/7QSL6IBFTKQMIZ3OLLNHFHRYOD.json","graph_json":"https://pith.science/api/pith-number/7QSL6IBFTKQMIZ3OLLNHFHRYOD/graph.json","events_json":"https://pith.science/api/pith-number/7QSL6IBFTKQMIZ3OLLNHFHRYOD/events.json","paper":"https://pith.science/paper/7QSL6IBF"},"agent_actions":{"view_html":"https://pith.science/pith/7QSL6IBFTKQMIZ3OLLNHFHRYOD","download_json":"https://pith.science/pith/7QSL6IBFTKQMIZ3OLLNHFHRYOD.json","view_paper":"https://pith.science/paper/7QSL6IBF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05504&json=true","fetch_graph":"https://pith.science/api/pith-number/7QSL6IBFTKQMIZ3OLLNHFHRYOD/graph.json","fetch_events":"https://pith.science/api/pith-number/7QSL6IBFTKQMIZ3OLLNHFHRYOD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QSL6IBFTKQMIZ3OLLNHFHRYOD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QSL6IBFTKQMIZ3OLLNHFHRYOD/action/storage_attestation","attest_author":"https://pith.science/pith/7QSL6IBFTKQMIZ3OLLNHFHRYOD/action/author_attestation","sign_citation":"https://pith.science/pith/7QSL6IBFTKQMIZ3OLLNHFHRYOD/action/citation_signature","submit_replication":"https://pith.science/pith/7QSL6IBFTKQMIZ3OLLNHFHRYOD/action/replication_record"}},"created_at":"2026-07-05T09:11:47.471969+00:00","updated_at":"2026-07-05T09:11:47.471969+00:00"}