{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RHPHKWM3DH7LX5QIYH7EJR57IV","short_pith_number":"pith:RHPHKWM3","schema_version":"1.0","canonical_sha256":"89de75599b19febbf608c1fe44c7bf4568386921b9e134b37cfc43aca2edb659","source":{"kind":"arxiv","id":"2110.14831","version":1},"attestation_state":"computed","paper":{"title":"The Balancing Act in Causal Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Avi Feller, David A. Hirshberg, Eli Ben-Michael, Jos\\'e R. Zubizarreta","submitted_at":"2021-10-28T00:51:16Z","abstract_excerpt":"The idea of covariate balance is at the core of causal inference. Inverse propensity weights play a central role because they are the unique set of weights that balance the covariate distributions of different treatment groups. We discuss two broad approaches to estimating these weights: the more traditional one, which fits a propensity score model and then uses the reciprocal of the estimated propensity score to construct weights, and the balancing approach, which estimates the inverse propensity weights essentially by the method of moments, finding weights that achieve balance in the sample."},"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":"2110.14831","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2021-10-28T00:51:16Z","cross_cats_sorted":[],"title_canon_sha256":"78135d049f878fffbb63979835aa0ce9148ffd9e310ba17276d441826615f908","abstract_canon_sha256":"fdde00ce5f1fd8c30f2f259dbb3a219338d79a6932c04e78d3d9fe2199d05598"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:26:47.894145Z","signature_b64":"yzioRM2djPaqkMaRgKDRTxWGIFOJCd58IZeOfzqGt6bpGLd98wKWDLghfCpw2dU8Hh9t1PIfS33TyLz+Y3JuDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89de75599b19febbf608c1fe44c7bf4568386921b9e134b37cfc43aca2edb659","last_reissued_at":"2026-07-05T03:26:47.893654Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:26:47.893654Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Balancing Act in Causal Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Avi Feller, David A. Hirshberg, Eli Ben-Michael, Jos\\'e R. Zubizarreta","submitted_at":"2021-10-28T00:51:16Z","abstract_excerpt":"The idea of covariate balance is at the core of causal inference. Inverse propensity weights play a central role because they are the unique set of weights that balance the covariate distributions of different treatment groups. We discuss two broad approaches to estimating these weights: the more traditional one, which fits a propensity score model and then uses the reciprocal of the estimated propensity score to construct weights, and the balancing approach, which estimates the inverse propensity weights essentially by the method of moments, finding weights that achieve balance in the sample."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.14831","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/2110.14831/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":"2110.14831","created_at":"2026-07-05T03:26:47.893711+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.14831v1","created_at":"2026-07-05T03:26:47.893711+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.14831","created_at":"2026-07-05T03:26:47.893711+00:00"},{"alias_kind":"pith_short_12","alias_value":"RHPHKWM3DH7L","created_at":"2026-07-05T03:26:47.893711+00:00"},{"alias_kind":"pith_short_16","alias_value":"RHPHKWM3DH7LX5QI","created_at":"2026-07-05T03:26:47.893711+00:00"},{"alias_kind":"pith_short_8","alias_value":"RHPHKWM3","created_at":"2026-07-05T03:26:47.893711+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06441","citing_title":"Leveraging External Controls for Treatment Switching in Randomized Controlled Trials: A Weighted Causal Inference Framework for Overall Survival","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2404.04794","citing_title":"Local Balance Calibration for Nonparametric Propensity Score Estimation","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07285","citing_title":"Transporting treatment effects by calibrating large-scale observational outcomes","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11415","citing_title":"Causal inference with ordinal outcomes: copula-based identification, estimation and sensitivity analysis","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06386","citing_title":"Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07285","citing_title":"Transporting treatment effects by calibrating large-scale observational outcomes","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV","json":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV.json","graph_json":"https://pith.science/api/pith-number/RHPHKWM3DH7LX5QIYH7EJR57IV/graph.json","events_json":"https://pith.science/api/pith-number/RHPHKWM3DH7LX5QIYH7EJR57IV/events.json","paper":"https://pith.science/paper/RHPHKWM3"},"agent_actions":{"view_html":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV","download_json":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV.json","view_paper":"https://pith.science/paper/RHPHKWM3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.14831&json=true","fetch_graph":"https://pith.science/api/pith-number/RHPHKWM3DH7LX5QIYH7EJR57IV/graph.json","fetch_events":"https://pith.science/api/pith-number/RHPHKWM3DH7LX5QIYH7EJR57IV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV/action/storage_attestation","attest_author":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV/action/author_attestation","sign_citation":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV/action/citation_signature","submit_replication":"https://pith.science/pith/RHPHKWM3DH7LX5QIYH7EJR57IV/action/replication_record"}},"created_at":"2026-07-05T03:26:47.893711+00:00","updated_at":"2026-07-05T03:26:47.893711+00:00"}