{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WKUEKNROJHAOGJXEOMJ26J5VII","short_pith_number":"pith:WKUEKNRO","schema_version":"1.0","canonical_sha256":"b2a845362e49c0e326e47313af27b542017434e8d8a65d8c26417083d050cfcc","source":{"kind":"arxiv","id":"2411.14003","version":2},"attestation_state":"computed","paper":{"title":"Generative Intervention Models for Causal Perturbation Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andreas Krause, Bernhard Sch\\\"olkopf, Lars Lorch, Niki Kilbertus, Nora Schneider","submitted_at":"2024-11-21T10:37:57Z","abstract_excerpt":"We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation are available. For example, in genomics, some properties of a drug may be known, but not their causal effects on the regulatory pathways of cells. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions in a jointly-estimated causal model. Contrary to prior approaches, this ena"},"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":"2411.14003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-21T10:37:57Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"649bf39c5c04a75ddde9c5b49b07b4c6cf4ae94410794e980baaf9b71011b2ea","abstract_canon_sha256":"815c48181a1f3f7b89edc9492b04a730290df069082bc3a6d59978a9116afed2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:34.209349Z","signature_b64":"B2lIJ2983xNA39f+ZRmco2B+v0TsbHVRsPrujNlQrDu/KL37BVBd2xv2wMa0ByzYTNpCJVwBSlIpM3Zu2oG0Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2a845362e49c0e326e47313af27b542017434e8d8a65d8c26417083d050cfcc","last_reissued_at":"2026-07-05T11:29:34.208828Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:34.208828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Intervention Models for Causal Perturbation Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andreas Krause, Bernhard Sch\\\"olkopf, Lars Lorch, Niki Kilbertus, Nora Schneider","submitted_at":"2024-11-21T10:37:57Z","abstract_excerpt":"We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation are available. For example, in genomics, some properties of a drug may be known, but not their causal effects on the regulatory pathways of cells. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions in a jointly-estimated causal model. Contrary to prior approaches, this ena"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14003","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.14003/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":"2411.14003","created_at":"2026-07-05T11:29:34.208893+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14003v2","created_at":"2026-07-05T11:29:34.208893+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14003","created_at":"2026-07-05T11:29:34.208893+00:00"},{"alias_kind":"pith_short_12","alias_value":"WKUEKNROJHAO","created_at":"2026-07-05T11:29:34.208893+00:00"},{"alias_kind":"pith_short_16","alias_value":"WKUEKNROJHAOGJXE","created_at":"2026-07-05T11:29:34.208893+00:00"},{"alias_kind":"pith_short_8","alias_value":"WKUEKNRO","created_at":"2026-07-05T11:29:34.208893+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/WKUEKNROJHAOGJXEOMJ26J5VII","json":"https://pith.science/pith/WKUEKNROJHAOGJXEOMJ26J5VII.json","graph_json":"https://pith.science/api/pith-number/WKUEKNROJHAOGJXEOMJ26J5VII/graph.json","events_json":"https://pith.science/api/pith-number/WKUEKNROJHAOGJXEOMJ26J5VII/events.json","paper":"https://pith.science/paper/WKUEKNRO"},"agent_actions":{"view_html":"https://pith.science/pith/WKUEKNROJHAOGJXEOMJ26J5VII","download_json":"https://pith.science/pith/WKUEKNROJHAOGJXEOMJ26J5VII.json","view_paper":"https://pith.science/paper/WKUEKNRO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14003&json=true","fetch_graph":"https://pith.science/api/pith-number/WKUEKNROJHAOGJXEOMJ26J5VII/graph.json","fetch_events":"https://pith.science/api/pith-number/WKUEKNROJHAOGJXEOMJ26J5VII/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WKUEKNROJHAOGJXEOMJ26J5VII/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WKUEKNROJHAOGJXEOMJ26J5VII/action/storage_attestation","attest_author":"https://pith.science/pith/WKUEKNROJHAOGJXEOMJ26J5VII/action/author_attestation","sign_citation":"https://pith.science/pith/WKUEKNROJHAOGJXEOMJ26J5VII/action/citation_signature","submit_replication":"https://pith.science/pith/WKUEKNROJHAOGJXEOMJ26J5VII/action/replication_record"}},"created_at":"2026-07-05T11:29:34.208893+00:00","updated_at":"2026-07-05T11:29:34.208893+00:00"}