{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:KFZX3ABMMA3ZRGPRJQCYTEYNBO","short_pith_number":"pith:KFZX3ABM","schema_version":"1.0","canonical_sha256":"51737d802c60379899f14c0589930d0bb741f40155d39ade4bd62535878b26bb","source":{"kind":"arxiv","id":"2002.12326","version":2},"attestation_state":"computed","paper":{"title":"Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ioana Bica, James Jordon, Mihaela van der Schaar","submitted_at":"2020-02-27T18:46:21Z","abstract_excerpt":"While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting of continuous-valued interventions, such as treatments associated with a dosage parameter. In this paper, we tackle this problem by building on a modification of the generative adversarial networks (GANs) framework. Our model, SCIGAN, is flexible and capable of simultaneously estimating counterfactual outcomes for several different continuous interventions. The key idea is to use a significantly modified GAN model to "},"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":"2002.12326","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-27T18:46:21Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6610ea61dbf6a36c4713933f1388bdf4e60fc8eab2aed5aa5305f1367f908789","abstract_canon_sha256":"1b5c534ca55940a747df5a3d2d214e4f8cff9e9e2e8fe4d5a2a1964e964445e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:53:19.200502Z","signature_b64":"tXOM3Af31akxWBXNUpxDbVrYtC/mdbiovCZ3okqevkP9HfART0MXXF+cZLjqXWG+Rpq4UL7atcBeLuwW3slWBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51737d802c60379899f14c0589930d0bb741f40155d39ade4bd62535878b26bb","last_reissued_at":"2026-07-05T01:53:19.200147Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:53:19.200147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ioana Bica, James Jordon, Mihaela van der Schaar","submitted_at":"2020-02-27T18:46:21Z","abstract_excerpt":"While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting of continuous-valued interventions, such as treatments associated with a dosage parameter. In this paper, we tackle this problem by building on a modification of the generative adversarial networks (GANs) framework. Our model, SCIGAN, is flexible and capable of simultaneously estimating counterfactual outcomes for several different continuous interventions. The key idea is to use a significantly modified GAN model to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.12326","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/2002.12326/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":"2002.12326","created_at":"2026-07-05T01:53:19.200197+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.12326v2","created_at":"2026-07-05T01:53:19.200197+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.12326","created_at":"2026-07-05T01:53:19.200197+00:00"},{"alias_kind":"pith_short_12","alias_value":"KFZX3ABMMA3Z","created_at":"2026-07-05T01:53:19.200197+00:00"},{"alias_kind":"pith_short_16","alias_value":"KFZX3ABMMA3ZRGPR","created_at":"2026-07-05T01:53:19.200197+00:00"},{"alias_kind":"pith_short_8","alias_value":"KFZX3ABM","created_at":"2026-07-05T01:53:19.200197+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/KFZX3ABMMA3ZRGPRJQCYTEYNBO","json":"https://pith.science/pith/KFZX3ABMMA3ZRGPRJQCYTEYNBO.json","graph_json":"https://pith.science/api/pith-number/KFZX3ABMMA3ZRGPRJQCYTEYNBO/graph.json","events_json":"https://pith.science/api/pith-number/KFZX3ABMMA3ZRGPRJQCYTEYNBO/events.json","paper":"https://pith.science/paper/KFZX3ABM"},"agent_actions":{"view_html":"https://pith.science/pith/KFZX3ABMMA3ZRGPRJQCYTEYNBO","download_json":"https://pith.science/pith/KFZX3ABMMA3ZRGPRJQCYTEYNBO.json","view_paper":"https://pith.science/paper/KFZX3ABM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.12326&json=true","fetch_graph":"https://pith.science/api/pith-number/KFZX3ABMMA3ZRGPRJQCYTEYNBO/graph.json","fetch_events":"https://pith.science/api/pith-number/KFZX3ABMMA3ZRGPRJQCYTEYNBO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KFZX3ABMMA3ZRGPRJQCYTEYNBO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KFZX3ABMMA3ZRGPRJQCYTEYNBO/action/storage_attestation","attest_author":"https://pith.science/pith/KFZX3ABMMA3ZRGPRJQCYTEYNBO/action/author_attestation","sign_citation":"https://pith.science/pith/KFZX3ABMMA3ZRGPRJQCYTEYNBO/action/citation_signature","submit_replication":"https://pith.science/pith/KFZX3ABMMA3ZRGPRJQCYTEYNBO/action/replication_record"}},"created_at":"2026-07-05T01:53:19.200197+00:00","updated_at":"2026-07-05T01:53:19.200197+00:00"}