{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2GPG4M3LT5ZH2LSURV6HF765DC","short_pith_number":"pith:2GPG4M3L","schema_version":"1.0","canonical_sha256":"d19e6e336b9f727d2e548d7c72ffdd1893aec4185489b0639d4527b1241738c2","source":{"kind":"arxiv","id":"2201.05893","version":2},"attestation_state":"computed","paper":{"title":"Treatment Effect Risk: Bounds and Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","math.OC","stat.ML"],"primary_cat":"stat.ME","authors_text":"Nathan Kallus","submitted_at":"2022-01-15T17:21:26Z","abstract_excerpt":"Since the average treatment effect (ATE) measures the change in social welfare, even if positive, there is a risk of negative effect on, say, some 10% of the population. Assessing such risk is difficult, however, because any one individual treatment effect (ITE) is never observed, so the 10% worst-affected cannot be identified, while distributional treatment effects only compare the first deciles within each treatment group, which does not correspond to any 10%-subpopulation. In this paper we consider how to nonetheless assess this important risk measure, formalized as the conditional value at"},"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":"2201.05893","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2022-01-15T17:21:26Z","cross_cats_sorted":["econ.EM","math.OC","stat.ML"],"title_canon_sha256":"858b40cc5361567a249aaf8345837c792c8df336031bad98b249b7d3a7d518e5","abstract_canon_sha256":"db7df6485f988c52dc7dc6bd5f50d6aef218b2ce4982794ad1cbd8ad5940ec67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:41:30.671245Z","signature_b64":"kZBh4yykPVyrFjZP6ZAS4/rnCVmqX5VmI2XcfgNTN+6/LodEGnTNU7tISmfqT9Ulfn0crLpC4H17aueOsy9BDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d19e6e336b9f727d2e548d7c72ffdd1893aec4185489b0639d4527b1241738c2","last_reissued_at":"2026-07-05T04:41:30.670778Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:41:30.670778Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Treatment Effect Risk: Bounds and Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","math.OC","stat.ML"],"primary_cat":"stat.ME","authors_text":"Nathan Kallus","submitted_at":"2022-01-15T17:21:26Z","abstract_excerpt":"Since the average treatment effect (ATE) measures the change in social welfare, even if positive, there is a risk of negative effect on, say, some 10% of the population. Assessing such risk is difficult, however, because any one individual treatment effect (ITE) is never observed, so the 10% worst-affected cannot be identified, while distributional treatment effects only compare the first deciles within each treatment group, which does not correspond to any 10%-subpopulation. In this paper we consider how to nonetheless assess this important risk measure, formalized as the conditional value at"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.05893","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/2201.05893/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":"2201.05893","created_at":"2026-07-05T04:41:30.670834+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.05893v2","created_at":"2026-07-05T04:41:30.670834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.05893","created_at":"2026-07-05T04:41:30.670834+00:00"},{"alias_kind":"pith_short_12","alias_value":"2GPG4M3LT5ZH","created_at":"2026-07-05T04:41:30.670834+00:00"},{"alias_kind":"pith_short_16","alias_value":"2GPG4M3LT5ZH2LSU","created_at":"2026-07-05T04:41:30.670834+00:00"},{"alias_kind":"pith_short_8","alias_value":"2GPG4M3L","created_at":"2026-07-05T04:41:30.670834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.07469","citing_title":"Individual Treatment Effect: Prediction Intervals and Sharp Bounds","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC","json":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC.json","graph_json":"https://pith.science/api/pith-number/2GPG4M3LT5ZH2LSURV6HF765DC/graph.json","events_json":"https://pith.science/api/pith-number/2GPG4M3LT5ZH2LSURV6HF765DC/events.json","paper":"https://pith.science/paper/2GPG4M3L"},"agent_actions":{"view_html":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC","download_json":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC.json","view_paper":"https://pith.science/paper/2GPG4M3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.05893&json=true","fetch_graph":"https://pith.science/api/pith-number/2GPG4M3LT5ZH2LSURV6HF765DC/graph.json","fetch_events":"https://pith.science/api/pith-number/2GPG4M3LT5ZH2LSURV6HF765DC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC/action/storage_attestation","attest_author":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC/action/author_attestation","sign_citation":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC/action/citation_signature","submit_replication":"https://pith.science/pith/2GPG4M3LT5ZH2LSURV6HF765DC/action/replication_record"}},"created_at":"2026-07-05T04:41:30.670834+00:00","updated_at":"2026-07-05T04:41:30.670834+00:00"}