{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:WHANJ2L6TWZIO4MUO6SL4DZAS5","short_pith_number":"pith:WHANJ2L6","schema_version":"1.0","canonical_sha256":"b1c0d4e97e9db287719477a4be0f20977696ab12c15184a42348a4e1b0464c3a","source":{"kind":"arxiv","id":"1905.00744","version":1},"attestation_state":"computed","paper":{"title":"Sparsity Double Robust Inference of Average Treatment Effects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Jelena Bradic, Stefan Wager, Yinchu Zhu","submitted_at":"2019-05-02T13:47:15Z","abstract_excerpt":"Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong \"ultra-sparsity\" assumptions that may be difficult to validate in practice. To alleviate this difficulty, we here study a new method for average treatment effect estimation that yields asymptotically exact confidence intervals assuming that either the conditional response surface or the conditional probability of treatment allows for an ultra-sparse representation (but not necessarily both). This guarantee allows us to provide valid inference for average treatment effect i"},"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":"1905.00744","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-05-02T13:47:15Z","cross_cats_sorted":["econ.EM","stat.ME","stat.TH"],"title_canon_sha256":"49b2fb18b410f2060aff0ebe6df94a54029f37d03940272dd19a22fafc64e959","abstract_canon_sha256":"fb448f0a0789722075cc6a24f9b79e6454b9b4f46c5707d1127d26d2396c1057"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:06.899980Z","signature_b64":"7OXE1BSdJjr18FV0Dl3q8NcEoGRE2pPOaVG3wP+tBdnj82onNGnvyw9IvSMYky+sXzVVYzJwG7hUuok/9ltZDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1c0d4e97e9db287719477a4be0f20977696ab12c15184a42348a4e1b0464c3a","last_reissued_at":"2026-05-17T23:47:06.899293Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:06.899293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparsity Double Robust Inference of Average Treatment Effects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Jelena Bradic, Stefan Wager, Yinchu Zhu","submitted_at":"2019-05-02T13:47:15Z","abstract_excerpt":"Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong \"ultra-sparsity\" assumptions that may be difficult to validate in practice. To alleviate this difficulty, we here study a new method for average treatment effect estimation that yields asymptotically exact confidence intervals assuming that either the conditional response surface or the conditional probability of treatment allows for an ultra-sparse representation (but not necessarily both). This guarantee allows us to provide valid inference for average treatment effect i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00744","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":""},"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":"1905.00744","created_at":"2026-05-17T23:47:06.899422+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.00744v1","created_at":"2026-05-17T23:47:06.899422+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00744","created_at":"2026-05-17T23:47:06.899422+00:00"},{"alias_kind":"pith_short_12","alias_value":"WHANJ2L6TWZI","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"WHANJ2L6TWZIO4MU","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"WHANJ2L6","created_at":"2026-05-18T12:33:30.264802+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/WHANJ2L6TWZIO4MUO6SL4DZAS5","json":"https://pith.science/pith/WHANJ2L6TWZIO4MUO6SL4DZAS5.json","graph_json":"https://pith.science/api/pith-number/WHANJ2L6TWZIO4MUO6SL4DZAS5/graph.json","events_json":"https://pith.science/api/pith-number/WHANJ2L6TWZIO4MUO6SL4DZAS5/events.json","paper":"https://pith.science/paper/WHANJ2L6"},"agent_actions":{"view_html":"https://pith.science/pith/WHANJ2L6TWZIO4MUO6SL4DZAS5","download_json":"https://pith.science/pith/WHANJ2L6TWZIO4MUO6SL4DZAS5.json","view_paper":"https://pith.science/paper/WHANJ2L6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.00744&json=true","fetch_graph":"https://pith.science/api/pith-number/WHANJ2L6TWZIO4MUO6SL4DZAS5/graph.json","fetch_events":"https://pith.science/api/pith-number/WHANJ2L6TWZIO4MUO6SL4DZAS5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WHANJ2L6TWZIO4MUO6SL4DZAS5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WHANJ2L6TWZIO4MUO6SL4DZAS5/action/storage_attestation","attest_author":"https://pith.science/pith/WHANJ2L6TWZIO4MUO6SL4DZAS5/action/author_attestation","sign_citation":"https://pith.science/pith/WHANJ2L6TWZIO4MUO6SL4DZAS5/action/citation_signature","submit_replication":"https://pith.science/pith/WHANJ2L6TWZIO4MUO6SL4DZAS5/action/replication_record"}},"created_at":"2026-05-17T23:47:06.899422+00:00","updated_at":"2026-05-17T23:47:06.899422+00:00"}