{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZVA2Z6PD22YI7VPVQGEFWMI7YF","short_pith_number":"pith:ZVA2Z6PD","schema_version":"1.0","canonical_sha256":"cd41acf9e3d6b08fd5f581885b311fc17b9d238147ea47d685844b33f6ec2c00","source":{"kind":"arxiv","id":"1806.01083","version":2},"attestation_state":"computed","paper":{"title":"Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"stat.ME","authors_text":"Michele Santacatterina, Nathan Kallus","submitted_at":"2018-06-04T12:51:05Z","abstract_excerpt":"Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting (IPTW) can lead to high-variance estimates due to extreme weights and be sensitive to model misspecification. Various methods have been proposed to partially address this, including truncation and stabilized-IPTW to temper extreme weights and covariate balancing propensity score (CBPS) to address treatment model misspecification. In this paper, we present Kernel O"},"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":"1806.01083","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-06-04T12:51:05Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"e7480f200932a0f304cddec90565136398bf3bf6dcf22797849c21e0f36b1169","abstract_canon_sha256":"34ab84b2cef9e9cca36d69ed482ffc6f85285ff5a71cdfc75a89c1b4df03eace"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:53:08.979694Z","signature_b64":"GBXuNArFgwWchX4NLC1ZqRn5nDGQfv+MPnrLpsfXZFpsJ+dDW+a4YnkxcBP+eiQJANhQ2WSvf9lUftFVbqBUBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd41acf9e3d6b08fd5f581885b311fc17b9d238147ea47d685844b33f6ec2c00","last_reissued_at":"2026-07-04T23:53:08.979194Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:53:08.979194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"stat.ME","authors_text":"Michele Santacatterina, Nathan Kallus","submitted_at":"2018-06-04T12:51:05Z","abstract_excerpt":"Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting (IPTW) can lead to high-variance estimates due to extreme weights and be sensitive to model misspecification. Various methods have been proposed to partially address this, including truncation and stabilized-IPTW to temper extreme weights and covariate balancing propensity score (CBPS) to address treatment model misspecification. In this paper, we present Kernel O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.01083","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/1806.01083/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":"1806.01083","created_at":"2026-07-04T23:53:08.979254+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.01083v2","created_at":"2026-07-04T23:53:08.979254+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.01083","created_at":"2026-07-04T23:53:08.979254+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZVA2Z6PD22YI","created_at":"2026-07-04T23:53:08.979254+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZVA2Z6PD22YI7VPV","created_at":"2026-07-04T23:53:08.979254+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZVA2Z6PD","created_at":"2026-07-04T23:53:08.979254+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.04748","citing_title":"Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF","json":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF.json","graph_json":"https://pith.science/api/pith-number/ZVA2Z6PD22YI7VPVQGEFWMI7YF/graph.json","events_json":"https://pith.science/api/pith-number/ZVA2Z6PD22YI7VPVQGEFWMI7YF/events.json","paper":"https://pith.science/paper/ZVA2Z6PD"},"agent_actions":{"view_html":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF","download_json":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF.json","view_paper":"https://pith.science/paper/ZVA2Z6PD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.01083&json=true","fetch_graph":"https://pith.science/api/pith-number/ZVA2Z6PD22YI7VPVQGEFWMI7YF/graph.json","fetch_events":"https://pith.science/api/pith-number/ZVA2Z6PD22YI7VPVQGEFWMI7YF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF/action/storage_attestation","attest_author":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF/action/author_attestation","sign_citation":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF/action/citation_signature","submit_replication":"https://pith.science/pith/ZVA2Z6PD22YI7VPVQGEFWMI7YF/action/replication_record"}},"created_at":"2026-07-04T23:53:08.979254+00:00","updated_at":"2026-07-04T23:53:08.979254+00:00"}