{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GAP7DCM22FK7ZQ2N35RPZBC7UQ","short_pith_number":"pith:GAP7DCM2","schema_version":"1.0","canonical_sha256":"301ff1899ad155fcc34ddf62fc845fa42b1530a7c36d8d0b7bb498f469498adf","source":{"kind":"arxiv","id":"2402.12576","version":1},"attestation_state":"computed","paper":{"title":"Understanding Difference-in-differences methods to evaluate policy effects with staggered adoption: an application to Medicaid and HIV","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Andrew J. Spieker, Bryan E. Shepherd, Julia C. Thome, Peter F. Rebeiro","submitted_at":"2024-02-19T21:57:40Z","abstract_excerpt":"While a randomized control trial is considered the gold standard for estimating causal treatment effects, there are many research settings in which randomization is infeasible or unethical. In such cases, researchers rely on analytical methods for observational data to explore causal relationships. Difference-in-differences (DID) is one such method that, most commonly, estimates a difference in some mean outcome in a group before and after the implementation of an intervention or policy and compares this with a control group followed over the same time (i.e., a group that did not implement the"},"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":"2402.12576","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2024-02-19T21:57:40Z","cross_cats_sorted":[],"title_canon_sha256":"34409b4fab5835648d410a86a6b0d87ad966c0faf83bf72c219e038bc01dca19","abstract_canon_sha256":"9be2b6084c706676f6375a298fbe7d4aea36dfbfb4cbabf9b551ca71d1f68252"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:13.196558Z","signature_b64":"Knd444r+3NR2/hGlGSsWhZd+TOAnJDrb1ciuCp+ip5kn0a7m67hRUOuJdQgROzNCAKa1PziBOIxZyLNDBRjdDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"301ff1899ad155fcc34ddf62fc845fa42b1530a7c36d8d0b7bb498f469498adf","last_reissued_at":"2026-07-05T07:47:13.196094Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:13.196094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Understanding Difference-in-differences methods to evaluate policy effects with staggered adoption: an application to Medicaid and HIV","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Andrew J. Spieker, Bryan E. Shepherd, Julia C. Thome, Peter F. Rebeiro","submitted_at":"2024-02-19T21:57:40Z","abstract_excerpt":"While a randomized control trial is considered the gold standard for estimating causal treatment effects, there are many research settings in which randomization is infeasible or unethical. In such cases, researchers rely on analytical methods for observational data to explore causal relationships. Difference-in-differences (DID) is one such method that, most commonly, estimates a difference in some mean outcome in a group before and after the implementation of an intervention or policy and compares this with a control group followed over the same time (i.e., a group that did not implement the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12576","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/2402.12576/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":"2402.12576","created_at":"2026-07-05T07:47:13.196152+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.12576v1","created_at":"2026-07-05T07:47:13.196152+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12576","created_at":"2026-07-05T07:47:13.196152+00:00"},{"alias_kind":"pith_short_12","alias_value":"GAP7DCM22FK7","created_at":"2026-07-05T07:47:13.196152+00:00"},{"alias_kind":"pith_short_16","alias_value":"GAP7DCM22FK7ZQ2N","created_at":"2026-07-05T07:47:13.196152+00:00"},{"alias_kind":"pith_short_8","alias_value":"GAP7DCM2","created_at":"2026-07-05T07:47:13.196152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.04699","citing_title":"A Meta-learner for Heterogeneous Effects in Difference-in-Differences","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ","json":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ.json","graph_json":"https://pith.science/api/pith-number/GAP7DCM22FK7ZQ2N35RPZBC7UQ/graph.json","events_json":"https://pith.science/api/pith-number/GAP7DCM22FK7ZQ2N35RPZBC7UQ/events.json","paper":"https://pith.science/paper/GAP7DCM2"},"agent_actions":{"view_html":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ","download_json":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ.json","view_paper":"https://pith.science/paper/GAP7DCM2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.12576&json=true","fetch_graph":"https://pith.science/api/pith-number/GAP7DCM22FK7ZQ2N35RPZBC7UQ/graph.json","fetch_events":"https://pith.science/api/pith-number/GAP7DCM22FK7ZQ2N35RPZBC7UQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ/action/storage_attestation","attest_author":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ/action/author_attestation","sign_citation":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ/action/citation_signature","submit_replication":"https://pith.science/pith/GAP7DCM22FK7ZQ2N35RPZBC7UQ/action/replication_record"}},"created_at":"2026-07-05T07:47:13.196152+00:00","updated_at":"2026-07-05T07:47:13.196152+00:00"}