{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CYFTGKTGB5MWQBYOG3EB27WXEG","short_pith_number":"pith:CYFTGKTG","schema_version":"1.0","canonical_sha256":"160b332a660f5968070e36c81d7ed721b079c62e9e111a707bd0783787b9388b","source":{"kind":"arxiv","id":"2310.17806","version":2},"attestation_state":"computed","paper":{"title":"Transporting treatment effects from difference-in-differences studies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Audrey Renson, Ellicott C. Matthay, Kara E. Rudolph","submitted_at":"2023-10-26T22:55:45Z","abstract_excerpt":"Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample average treatment effect in the treated (SATT). However, a goal of such research is often to inform decision-making in target populations outside the treated sample. Transportability methods have been developed to extend inferences from study samples to external target populations; these methods have primarily been developed and applied in settings where identification is based on conditional independence between th"},"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":"2310.17806","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2023-10-26T22:55:45Z","cross_cats_sorted":[],"title_canon_sha256":"024012ee36fc2cad09c896d2cb69d19c6e2cf9211add0730ac91a2b118f1c9ad","abstract_canon_sha256":"8ff7d85409ec3074a58deb8bf63dc4097c9f299d85c33f94aeaad6d4ac42eac5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:34:07.132149Z","signature_b64":"9taWa0iMwt8ArxORrRKWL+0dI6x0PBOhcR8hsPVtXbUyeHPDfm0qKPkF6FyuMaFB+2D9E0fE+KQW7M/6KJA8AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"160b332a660f5968070e36c81d7ed721b079c62e9e111a707bd0783787b9388b","last_reissued_at":"2026-07-05T08:34:07.131661Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:34:07.131661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transporting treatment effects from difference-in-differences studies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Audrey Renson, Ellicott C. Matthay, Kara E. Rudolph","submitted_at":"2023-10-26T22:55:45Z","abstract_excerpt":"Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample average treatment effect in the treated (SATT). However, a goal of such research is often to inform decision-making in target populations outside the treated sample. Transportability methods have been developed to extend inferences from study samples to external target populations; these methods have primarily been developed and applied in settings where identification is based on conditional independence between th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.17806","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/2310.17806/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":"2310.17806","created_at":"2026-07-05T08:34:07.131717+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.17806v2","created_at":"2026-07-05T08:34:07.131717+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.17806","created_at":"2026-07-05T08:34:07.131717+00:00"},{"alias_kind":"pith_short_12","alias_value":"CYFTGKTGB5MW","created_at":"2026-07-05T08:34:07.131717+00:00"},{"alias_kind":"pith_short_16","alias_value":"CYFTGKTGB5MWQBYO","created_at":"2026-07-05T08:34:07.131717+00:00"},{"alias_kind":"pith_short_8","alias_value":"CYFTGKTG","created_at":"2026-07-05T08:34:07.131717+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.05363","citing_title":"Pulling back the curtain: the road from statistical estimand to machine-learning based estimator for epidemiologists (no wizard required)","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG","json":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG.json","graph_json":"https://pith.science/api/pith-number/CYFTGKTGB5MWQBYOG3EB27WXEG/graph.json","events_json":"https://pith.science/api/pith-number/CYFTGKTGB5MWQBYOG3EB27WXEG/events.json","paper":"https://pith.science/paper/CYFTGKTG"},"agent_actions":{"view_html":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG","download_json":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG.json","view_paper":"https://pith.science/paper/CYFTGKTG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.17806&json=true","fetch_graph":"https://pith.science/api/pith-number/CYFTGKTGB5MWQBYOG3EB27WXEG/graph.json","fetch_events":"https://pith.science/api/pith-number/CYFTGKTGB5MWQBYOG3EB27WXEG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG/action/storage_attestation","attest_author":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG/action/author_attestation","sign_citation":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG/action/citation_signature","submit_replication":"https://pith.science/pith/CYFTGKTGB5MWQBYOG3EB27WXEG/action/replication_record"}},"created_at":"2026-07-05T08:34:07.131717+00:00","updated_at":"2026-07-05T08:34:07.131717+00:00"}