{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:O3DEFKTIPF3PU6L5WFQBTSGMXZ","short_pith_number":"pith:O3DEFKTI","schema_version":"1.0","canonical_sha256":"76c642aa687976fa797db16019c8ccbe7428cee466f31d002437c500338b55fc","source":{"kind":"arxiv","id":"1810.03030","version":1},"attestation_state":"computed","paper":{"title":"Robust variance estimation and inference for causal effect estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Joshua Schwab, Linh Tran, Mark J van der Laan, Maya Petersen","submitted_at":"2018-10-06T17:40:30Z","abstract_excerpt":"We consider a longitudinal data structure consisting of baseline covariates, time-varying treatment variables, intermediate time-dependent covariates, and a possibly time dependent outcome. Previous studies have shown that estimating the variance of asymptotically linear estimators using empirical influence functions in this setting result in anti-conservative estimates with increasing magnitudes of positivity violations, leading to poor coverage and uncontrolled Type I errors. In this paper, we present two alternative approaches of estimating the variance of these estimators: (i) a robust app"},"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":"1810.03030","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-10-06T17:40:30Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"81337b9d370fa8193e35bd1d64a791c5367938f8bb2562998c7e59f002e54aa8","abstract_canon_sha256":"a7d010b61acadfefd29303adf328ada667067bfe3ae1668de430c4586ffa5758"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:54.772163Z","signature_b64":"f/3Hlar4m3tQvW/AImt65ge1h1z6qPzyR0EoLq2ESiSs4Gt5YQ6IwkjmDuECCLqdYY6ywFMhyD7Wox8fQ2pFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76c642aa687976fa797db16019c8ccbe7428cee466f31d002437c500338b55fc","last_reissued_at":"2026-05-18T00:03:54.771524Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:54.771524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust variance estimation and inference for causal effect estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Joshua Schwab, Linh Tran, Mark J van der Laan, Maya Petersen","submitted_at":"2018-10-06T17:40:30Z","abstract_excerpt":"We consider a longitudinal data structure consisting of baseline covariates, time-varying treatment variables, intermediate time-dependent covariates, and a possibly time dependent outcome. Previous studies have shown that estimating the variance of asymptotically linear estimators using empirical influence functions in this setting result in anti-conservative estimates with increasing magnitudes of positivity violations, leading to poor coverage and uncontrolled Type I errors. In this paper, we present two alternative approaches of estimating the variance of these estimators: (i) a robust app"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.03030","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":"1810.03030","created_at":"2026-05-18T00:03:54.771617+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.03030v1","created_at":"2026-05-18T00:03:54.771617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.03030","created_at":"2026-05-18T00:03:54.771617+00:00"},{"alias_kind":"pith_short_12","alias_value":"O3DEFKTIPF3P","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"O3DEFKTIPF3PU6L5","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"O3DEFKTI","created_at":"2026-05-18T12:32:40.477152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.20615","citing_title":"Evaluating causal indirect effects when mediators are left-censored by assay limit of quantification","ref_index":189,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ","json":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ.json","graph_json":"https://pith.science/api/pith-number/O3DEFKTIPF3PU6L5WFQBTSGMXZ/graph.json","events_json":"https://pith.science/api/pith-number/O3DEFKTIPF3PU6L5WFQBTSGMXZ/events.json","paper":"https://pith.science/paper/O3DEFKTI"},"agent_actions":{"view_html":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ","download_json":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ.json","view_paper":"https://pith.science/paper/O3DEFKTI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.03030&json=true","fetch_graph":"https://pith.science/api/pith-number/O3DEFKTIPF3PU6L5WFQBTSGMXZ/graph.json","fetch_events":"https://pith.science/api/pith-number/O3DEFKTIPF3PU6L5WFQBTSGMXZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ/action/storage_attestation","attest_author":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ/action/author_attestation","sign_citation":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ/action/citation_signature","submit_replication":"https://pith.science/pith/O3DEFKTIPF3PU6L5WFQBTSGMXZ/action/replication_record"}},"created_at":"2026-05-18T00:03:54.771617+00:00","updated_at":"2026-05-18T00:03:54.771617+00:00"}