{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:U4XNPDFQTQNVBT5BTEJLXZJL5B","short_pith_number":"pith:U4XNPDFQ","schema_version":"1.0","canonical_sha256":"a72ed78cb09c1b50cfa19912bbe52be86ead82f54e74569828ecd645560d88e4","source":{"kind":"arxiv","id":"2509.01352","version":1},"attestation_state":"computed","paper":{"title":"Causal Sensitivity Identification using Generative Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Soma Bandyopadhyay, Sudeshna Sarkar","submitted_at":"2025-09-01T10:42:44Z","abstract_excerpt":"In this work, we propose a novel generative method to identify the causal impact and apply it to prediction tasks. We conduct causal impact analysis using interventional and counterfactual perspectives. First, applying interventions, we identify features that have a causal influence on the predicted outcome, which we refer to as causally sensitive features, and second, applying counterfactuals, we evaluate how changes in the cause affect the effect. Our method exploits the Conditional Variational Autoencoder (CVAE) to identify the causal impact and serve as a generative predictor. We are able "},"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":"2509.01352","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-01T10:42:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"732bab9390235fe682823c15a642f80f48024535384b7eebdbf5503c06d08faf","abstract_canon_sha256":"bd821d9f73bce50f81195405feec260eef0140f60c6331c8191d6666ad52ee5a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:58.827204Z","signature_b64":"ESqd9yB6nEc44CQYc762Lld0T47g/9kXaD61+Khbqn01G5C2iAmICUTbehbu2xlou6bNvy80d/sfpWenr0uqBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a72ed78cb09c1b50cfa19912bbe52be86ead82f54e74569828ecd645560d88e4","last_reissued_at":"2026-07-05T12:02:58.826698Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:58.826698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Causal Sensitivity Identification using Generative Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Soma Bandyopadhyay, Sudeshna Sarkar","submitted_at":"2025-09-01T10:42:44Z","abstract_excerpt":"In this work, we propose a novel generative method to identify the causal impact and apply it to prediction tasks. We conduct causal impact analysis using interventional and counterfactual perspectives. First, applying interventions, we identify features that have a causal influence on the predicted outcome, which we refer to as causally sensitive features, and second, applying counterfactuals, we evaluate how changes in the cause affect the effect. Our method exploits the Conditional Variational Autoencoder (CVAE) to identify the causal impact and serve as a generative predictor. We are able "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.01352","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/2509.01352/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":"2509.01352","created_at":"2026-07-05T12:02:58.826764+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.01352v1","created_at":"2026-07-05T12:02:58.826764+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.01352","created_at":"2026-07-05T12:02:58.826764+00:00"},{"alias_kind":"pith_short_12","alias_value":"U4XNPDFQTQNV","created_at":"2026-07-05T12:02:58.826764+00:00"},{"alias_kind":"pith_short_16","alias_value":"U4XNPDFQTQNVBT5B","created_at":"2026-07-05T12:02:58.826764+00:00"},{"alias_kind":"pith_short_8","alias_value":"U4XNPDFQ","created_at":"2026-07-05T12:02:58.826764+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/U4XNPDFQTQNVBT5BTEJLXZJL5B","json":"https://pith.science/pith/U4XNPDFQTQNVBT5BTEJLXZJL5B.json","graph_json":"https://pith.science/api/pith-number/U4XNPDFQTQNVBT5BTEJLXZJL5B/graph.json","events_json":"https://pith.science/api/pith-number/U4XNPDFQTQNVBT5BTEJLXZJL5B/events.json","paper":"https://pith.science/paper/U4XNPDFQ"},"agent_actions":{"view_html":"https://pith.science/pith/U4XNPDFQTQNVBT5BTEJLXZJL5B","download_json":"https://pith.science/pith/U4XNPDFQTQNVBT5BTEJLXZJL5B.json","view_paper":"https://pith.science/paper/U4XNPDFQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.01352&json=true","fetch_graph":"https://pith.science/api/pith-number/U4XNPDFQTQNVBT5BTEJLXZJL5B/graph.json","fetch_events":"https://pith.science/api/pith-number/U4XNPDFQTQNVBT5BTEJLXZJL5B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U4XNPDFQTQNVBT5BTEJLXZJL5B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U4XNPDFQTQNVBT5BTEJLXZJL5B/action/storage_attestation","attest_author":"https://pith.science/pith/U4XNPDFQTQNVBT5BTEJLXZJL5B/action/author_attestation","sign_citation":"https://pith.science/pith/U4XNPDFQTQNVBT5BTEJLXZJL5B/action/citation_signature","submit_replication":"https://pith.science/pith/U4XNPDFQTQNVBT5BTEJLXZJL5B/action/replication_record"}},"created_at":"2026-07-05T12:02:58.826764+00:00","updated_at":"2026-07-05T12:02:58.826764+00:00"}