{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UWQT7BQGQ3DCCMG5CFR5DXWYME","short_pith_number":"pith:UWQT7BQG","schema_version":"1.0","canonical_sha256":"a5a13f860686c62130dd1163d1ded8611f330ab4d57979f6d1f677076efdba4a","source":{"kind":"arxiv","id":"2506.12869","version":1},"attestation_state":"computed","paper":{"title":"Finite sample-optimal adjustment sets in linear Gaussian causal models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Nadja Rutsch, Sara Magliacane, St\\'ephanie van der Pas","submitted_at":"2025-06-15T14:48:07Z","abstract_excerpt":"Traditional covariate selection methods for causal inference focus on achieving unbiasedness and asymptotic efficiency. In many practical scenarios, researchers must estimate causal effects from observational data with limited sample sizes or in cases where covariates are difficult or costly to measure. Their needs might be better met by selecting adjustment sets that are finite sample-optimal in terms of mean squared error. In this paper, we aim to find the adjustment set that minimizes the mean squared error of the causal effect estimator, taking into account the joint distribution of the va"},"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":"2506.12869","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-06-15T14:48:07Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"5d35aa23365946aeacda2ed96bcb937eb882c65ed2b577b6a1c2aa71beeacb16","abstract_canon_sha256":"31012c85d8d8ffb902ff540c949da1ce652e32dc973f16c08a249214df1f8fa5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:01.598640Z","signature_b64":"xhhG4T4fVnV+oiiIH2IeKqIDj01kNM5fWHjoidPhy/iIqKIPHcac9AWNpzQXSNI8YRpaZ0lAEm/pvhuPx2WwDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5a13f860686c62130dd1163d1ded8611f330ab4d57979f6d1f677076efdba4a","last_reissued_at":"2026-07-05T11:22:01.598121Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:01.598121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finite sample-optimal adjustment sets in linear Gaussian causal models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Nadja Rutsch, Sara Magliacane, St\\'ephanie van der Pas","submitted_at":"2025-06-15T14:48:07Z","abstract_excerpt":"Traditional covariate selection methods for causal inference focus on achieving unbiasedness and asymptotic efficiency. In many practical scenarios, researchers must estimate causal effects from observational data with limited sample sizes or in cases where covariates are difficult or costly to measure. Their needs might be better met by selecting adjustment sets that are finite sample-optimal in terms of mean squared error. In this paper, we aim to find the adjustment set that minimizes the mean squared error of the causal effect estimator, taking into account the joint distribution of the va"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12869","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/2506.12869/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":"2506.12869","created_at":"2026-07-05T11:22:01.598189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12869v1","created_at":"2026-07-05T11:22:01.598189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12869","created_at":"2026-07-05T11:22:01.598189+00:00"},{"alias_kind":"pith_short_12","alias_value":"UWQT7BQGQ3DC","created_at":"2026-07-05T11:22:01.598189+00:00"},{"alias_kind":"pith_short_16","alias_value":"UWQT7BQGQ3DCCMG5","created_at":"2026-07-05T11:22:01.598189+00:00"},{"alias_kind":"pith_short_8","alias_value":"UWQT7BQG","created_at":"2026-07-05T11:22:01.598189+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/UWQT7BQGQ3DCCMG5CFR5DXWYME","json":"https://pith.science/pith/UWQT7BQGQ3DCCMG5CFR5DXWYME.json","graph_json":"https://pith.science/api/pith-number/UWQT7BQGQ3DCCMG5CFR5DXWYME/graph.json","events_json":"https://pith.science/api/pith-number/UWQT7BQGQ3DCCMG5CFR5DXWYME/events.json","paper":"https://pith.science/paper/UWQT7BQG"},"agent_actions":{"view_html":"https://pith.science/pith/UWQT7BQGQ3DCCMG5CFR5DXWYME","download_json":"https://pith.science/pith/UWQT7BQGQ3DCCMG5CFR5DXWYME.json","view_paper":"https://pith.science/paper/UWQT7BQG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12869&json=true","fetch_graph":"https://pith.science/api/pith-number/UWQT7BQGQ3DCCMG5CFR5DXWYME/graph.json","fetch_events":"https://pith.science/api/pith-number/UWQT7BQGQ3DCCMG5CFR5DXWYME/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UWQT7BQGQ3DCCMG5CFR5DXWYME/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UWQT7BQGQ3DCCMG5CFR5DXWYME/action/storage_attestation","attest_author":"https://pith.science/pith/UWQT7BQGQ3DCCMG5CFR5DXWYME/action/author_attestation","sign_citation":"https://pith.science/pith/UWQT7BQGQ3DCCMG5CFR5DXWYME/action/citation_signature","submit_replication":"https://pith.science/pith/UWQT7BQGQ3DCCMG5CFR5DXWYME/action/replication_record"}},"created_at":"2026-07-05T11:22:01.598189+00:00","updated_at":"2026-07-05T11:22:01.598189+00:00"}