{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RBA4AXCC3EVCRGFLQLGL545AVV","short_pith_number":"pith:RBA4AXCC","schema_version":"1.0","canonical_sha256":"8841c05c42d92a2898ab82ccbef3a0ad79bf94b2638a999ce2cdf179f565ecf7","source":{"kind":"arxiv","id":"2605.27281","version":1},"attestation_state":"computed","paper":{"title":"Causal Risk Minimization for High-Dimensional Treatments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew Kim, Arnav Paruthi, Chris J. Maddison, Jekaterina Novikova, Lovedeep Gondara, Nikita Dhawan","submitted_at":"2026-05-26T16:58:39Z","abstract_excerpt":"Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains. However, classical causal estimators tend to assume that all possible interventions are observed, which is infeasible when interventions vary widely, for instance, in the space of all text strings. We adapt a well-known approach of recasting causal inference as a learning problem, to address high-dimensional treatment spaces. Specifically, under standard assumptio"},"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":"2605.27281","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-26T16:58:39Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"484406a37c47ef5cd28abe6f4e35efea72be730790eda84a16cdfce54acef912","abstract_canon_sha256":"1da1199058e4ce7ce2095725548ce11e915809bb0a7375b1aee864eefdfa2def"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T02:06:15.195874Z","signature_b64":"Wxu9MR7BALJQH+OnbxsJ81nhvPQNnoHMtK0jGVzLNvulqDO9boTD2lxGCLDYHPQ+QD302TK7ke0fL4Mx7Q1vBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8841c05c42d92a2898ab82ccbef3a0ad79bf94b2638a999ce2cdf179f565ecf7","last_reissued_at":"2026-05-27T02:06:15.194691Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T02:06:15.194691Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Causal Risk Minimization for High-Dimensional Treatments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew Kim, Arnav Paruthi, Chris J. Maddison, Jekaterina Novikova, Lovedeep Gondara, Nikita Dhawan","submitted_at":"2026-05-26T16:58:39Z","abstract_excerpt":"Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains. However, classical causal estimators tend to assume that all possible interventions are observed, which is infeasible when interventions vary widely, for instance, in the space of all text strings. We adapt a well-known approach of recasting causal inference as a learning problem, to address high-dimensional treatment spaces. Specifically, under standard assumptio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27281","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/2605.27281/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":"2605.27281","created_at":"2026-05-27T02:06:15.194827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.27281v1","created_at":"2026-05-27T02:06:15.194827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27281","created_at":"2026-05-27T02:06:15.194827+00:00"},{"alias_kind":"pith_short_12","alias_value":"RBA4AXCC3EVC","created_at":"2026-05-27T02:06:15.194827+00:00"},{"alias_kind":"pith_short_16","alias_value":"RBA4AXCC3EVCRGFL","created_at":"2026-05-27T02:06:15.194827+00:00"},{"alias_kind":"pith_short_8","alias_value":"RBA4AXCC","created_at":"2026-05-27T02:06:15.194827+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/RBA4AXCC3EVCRGFLQLGL545AVV","json":"https://pith.science/pith/RBA4AXCC3EVCRGFLQLGL545AVV.json","graph_json":"https://pith.science/api/pith-number/RBA4AXCC3EVCRGFLQLGL545AVV/graph.json","events_json":"https://pith.science/api/pith-number/RBA4AXCC3EVCRGFLQLGL545AVV/events.json","paper":"https://pith.science/paper/RBA4AXCC"},"agent_actions":{"view_html":"https://pith.science/pith/RBA4AXCC3EVCRGFLQLGL545AVV","download_json":"https://pith.science/pith/RBA4AXCC3EVCRGFLQLGL545AVV.json","view_paper":"https://pith.science/paper/RBA4AXCC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.27281&json=true","fetch_graph":"https://pith.science/api/pith-number/RBA4AXCC3EVCRGFLQLGL545AVV/graph.json","fetch_events":"https://pith.science/api/pith-number/RBA4AXCC3EVCRGFLQLGL545AVV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RBA4AXCC3EVCRGFLQLGL545AVV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RBA4AXCC3EVCRGFLQLGL545AVV/action/storage_attestation","attest_author":"https://pith.science/pith/RBA4AXCC3EVCRGFLQLGL545AVV/action/author_attestation","sign_citation":"https://pith.science/pith/RBA4AXCC3EVCRGFLQLGL545AVV/action/citation_signature","submit_replication":"https://pith.science/pith/RBA4AXCC3EVCRGFLQLGL545AVV/action/replication_record"}},"created_at":"2026-05-27T02:06:15.194827+00:00","updated_at":"2026-05-27T02:06:15.194827+00:00"}