{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:G5ZBHFZVUK5XLU67MAQVSTYRFB","short_pith_number":"pith:G5ZBHFZV","schema_version":"1.0","canonical_sha256":"3772139735a2bb75d3df6021594f112862bd388b22bf304104b087722003fba4","source":{"kind":"arxiv","id":"2306.16297","version":4},"attestation_state":"computed","paper":{"title":"A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Jieru Shi, Walter Dempsey","submitted_at":"2023-06-28T15:19:33Z","abstract_excerpt":"Advances in wearable technologies and health interventions delivered by smartphones have greatly increased the accessibility of mobile health (mHealth) interventions. Micro-randomized trials (MRTs) are designed to assess the effectiveness of the mHealth intervention and introduce a novel class of causal estimands called \"causal excursion effects.\" These estimands enable the evaluation of how intervention effects change over time and are influenced by individual characteristics or context. Existing methods for analyzing causal excursion effects assume known randomization probabilities, complete"},"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":"2306.16297","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2023-06-28T15:19:33Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ed3e21f1d15d558b70d8c119a87139988711f2d21802480f7bc79259147945e7","abstract_canon_sha256":"6df11be457b984e6541fa780e93ffabb64390b715acd962529f8149af71eb32d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:51:13.326542Z","signature_b64":"5czvOq7G85gDMpHNDNO+FnTvj8AxLF77li/u/ONudZE4a5nUMbLdResMYaXvFa2FNw9fTCTKih/TKM0WmYmnAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3772139735a2bb75d3df6021594f112862bd388b22bf304104b087722003fba4","last_reissued_at":"2026-07-05T11:51:13.326050Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:51:13.326050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Jieru Shi, Walter Dempsey","submitted_at":"2023-06-28T15:19:33Z","abstract_excerpt":"Advances in wearable technologies and health interventions delivered by smartphones have greatly increased the accessibility of mobile health (mHealth) interventions. Micro-randomized trials (MRTs) are designed to assess the effectiveness of the mHealth intervention and introduce a novel class of causal estimands called \"causal excursion effects.\" These estimands enable the evaluation of how intervention effects change over time and are influenced by individual characteristics or context. Existing methods for analyzing causal excursion effects assume known randomization probabilities, complete"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.16297","kind":"arxiv","version":4},"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/2306.16297/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":"2306.16297","created_at":"2026-07-05T11:51:13.326110+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.16297v4","created_at":"2026-07-05T11:51:13.326110+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.16297","created_at":"2026-07-05T11:51:13.326110+00:00"},{"alias_kind":"pith_short_12","alias_value":"G5ZBHFZVUK5X","created_at":"2026-07-05T11:51:13.326110+00:00"},{"alias_kind":"pith_short_16","alias_value":"G5ZBHFZVUK5XLU67","created_at":"2026-07-05T11:51:13.326110+00:00"},{"alias_kind":"pith_short_8","alias_value":"G5ZBHFZV","created_at":"2026-07-05T11:51:13.326110+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.06654","citing_title":"A Novel Tool for Evaluating Effect Modification in Older Adults with ADRD Using Medicare Claims","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB","json":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB.json","graph_json":"https://pith.science/api/pith-number/G5ZBHFZVUK5XLU67MAQVSTYRFB/graph.json","events_json":"https://pith.science/api/pith-number/G5ZBHFZVUK5XLU67MAQVSTYRFB/events.json","paper":"https://pith.science/paper/G5ZBHFZV"},"agent_actions":{"view_html":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB","download_json":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB.json","view_paper":"https://pith.science/paper/G5ZBHFZV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.16297&json=true","fetch_graph":"https://pith.science/api/pith-number/G5ZBHFZVUK5XLU67MAQVSTYRFB/graph.json","fetch_events":"https://pith.science/api/pith-number/G5ZBHFZVUK5XLU67MAQVSTYRFB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB/action/storage_attestation","attest_author":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB/action/author_attestation","sign_citation":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB/action/citation_signature","submit_replication":"https://pith.science/pith/G5ZBHFZVUK5XLU67MAQVSTYRFB/action/replication_record"}},"created_at":"2026-07-05T11:51:13.326110+00:00","updated_at":"2026-07-05T11:51:13.326110+00:00"}