{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VZ65KELFLUCWVC5QWZHU3ZG3EP","short_pith_number":"pith:VZ65KELF","canonical_record":{"source":{"id":"2406.16728","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-06-24T15:33:47Z","cross_cats_sorted":[],"title_canon_sha256":"5cb3dc4d3dfeaf0b66e47003b1634b1496deeaacf9bafe1d4097b533d3b7d30e","abstract_canon_sha256":"eaee504839ef0b93be087e7fc2e096f77f4353814cbcb3eee046a7801222d2ed"},"schema_version":"1.0"},"canonical_sha256":"ae7dd511655d056a8bb0b64f4de4db23faa9b981cf38dc27e29277c6d4eeb559","source":{"kind":"arxiv","id":"2406.16728","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.16728","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"arxiv_version","alias_value":"2406.16728v1","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.16728","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"pith_short_12","alias_value":"VZ65KELFLUCW","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"pith_short_16","alias_value":"VZ65KELFLUCWVC5Q","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"pith_short_8","alias_value":"VZ65KELF","created_at":"2026-07-05T08:36:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VZ65KELFLUCWVC5QWZHU3ZG3EP","target":"record","payload":{"canonical_record":{"source":{"id":"2406.16728","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-06-24T15:33:47Z","cross_cats_sorted":[],"title_canon_sha256":"5cb3dc4d3dfeaf0b66e47003b1634b1496deeaacf9bafe1d4097b533d3b7d30e","abstract_canon_sha256":"eaee504839ef0b93be087e7fc2e096f77f4353814cbcb3eee046a7801222d2ed"},"schema_version":"1.0"},"canonical_sha256":"ae7dd511655d056a8bb0b64f4de4db23faa9b981cf38dc27e29277c6d4eeb559","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:02.921887Z","signature_b64":"0Xx3A5UeQl4hedgdJqgSteKakVWrk7DbtxgvBcRDOcYuAFaLveRj+UTAqdtccuP569jVSpzONq71RE8TFToQBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae7dd511655d056a8bb0b64f4de4db23faa9b981cf38dc27e29277c6d4eeb559","last_reissued_at":"2026-07-05T08:36:02.921450Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:02.921450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.16728","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:36:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2GRnshAS2lPNRv1jzALZvSe2ud94cFxxdy9kvV/GmqnHxUu3rWgLscJcfc/dlSe2TuYZQhgvUw788+mlEhrYCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T10:47:39.630793Z"},"content_sha256":"27365d14b61ab54f37610e0defc01e80095263d3310139e443014725b14ebf82","schema_version":"1.0","event_id":"sha256:27365d14b61ab54f37610e0defc01e80095263d3310139e443014725b14ebf82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VZ65KELFLUCWVC5QWZHU3ZG3EP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CausalMMM: Learning Causal Structure for Marketing Mix Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chang Gong, Di Yao, Jingping Bi, Lei Zhang, Sheng Chen, Wenbin Li, Yueyang Su","submitted_at":"2024-06-24T15:33:47Z","abstract_excerpt":"In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocation of various advertising channels. Traditional MMM methods leveraging regression techniques can fail in handling the complexity of marketing. Although some efforts try to encode the causal structures for better prediction, they have the strict restriction that causal structures are prior-known and unchangeable. In this paper, we define a new causal MMM problem that automatically discovers the interpretable causal str"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.16728","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/2406.16728/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:36:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+oY8Sit4YrmVDjgOa8VFhBJXGcX9lcvQwpgJrN4FzoPDZDnSMlGFcb/O4hYRMehpGJXz6IA/8otmeITZ/zlICw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T10:47:39.631256Z"},"content_sha256":"1e571357e227ba3d4d441e195236a93f84977408d8c825dfcbdc52e89009fc83","schema_version":"1.0","event_id":"sha256:1e571357e227ba3d4d441e195236a93f84977408d8c825dfcbdc52e89009fc83"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VZ65KELFLUCWVC5QWZHU3ZG3EP/bundle.json","state_url":"https://pith.science/pith/VZ65KELFLUCWVC5QWZHU3ZG3EP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VZ65KELFLUCWVC5QWZHU3ZG3EP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T10:47:39Z","links":{"resolver":"https://pith.science/pith/VZ65KELFLUCWVC5QWZHU3ZG3EP","bundle":"https://pith.science/pith/VZ65KELFLUCWVC5QWZHU3ZG3EP/bundle.json","state":"https://pith.science/pith/VZ65KELFLUCWVC5QWZHU3ZG3EP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VZ65KELFLUCWVC5QWZHU3ZG3EP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VZ65KELFLUCWVC5QWZHU3ZG3EP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"eaee504839ef0b93be087e7fc2e096f77f4353814cbcb3eee046a7801222d2ed","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-06-24T15:33:47Z","title_canon_sha256":"5cb3dc4d3dfeaf0b66e47003b1634b1496deeaacf9bafe1d4097b533d3b7d30e"},"schema_version":"1.0","source":{"id":"2406.16728","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.16728","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"arxiv_version","alias_value":"2406.16728v1","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.16728","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"pith_short_12","alias_value":"VZ65KELFLUCW","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"pith_short_16","alias_value":"VZ65KELFLUCWVC5Q","created_at":"2026-07-05T08:36:02Z"},{"alias_kind":"pith_short_8","alias_value":"VZ65KELF","created_at":"2026-07-05T08:36:02Z"}],"graph_snapshots":[{"event_id":"sha256:1e571357e227ba3d4d441e195236a93f84977408d8c825dfcbdc52e89009fc83","target":"graph","created_at":"2026-07-05T08:36:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.16728/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocation of various advertising channels. Traditional MMM methods leveraging regression techniques can fail in handling the complexity of marketing. Although some efforts try to encode the causal structures for better prediction, they have the strict restriction that causal structures are prior-known and unchangeable. In this paper, we define a new causal MMM problem that automatically discovers the interpretable causal str","authors_text":"Chang Gong, Di Yao, Jingping Bi, Lei Zhang, Sheng Chen, Wenbin Li, Yueyang Su","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-06-24T15:33:47Z","title":"CausalMMM: Learning Causal Structure for Marketing Mix Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.16728","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:27365d14b61ab54f37610e0defc01e80095263d3310139e443014725b14ebf82","target":"record","created_at":"2026-07-05T08:36:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"eaee504839ef0b93be087e7fc2e096f77f4353814cbcb3eee046a7801222d2ed","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-06-24T15:33:47Z","title_canon_sha256":"5cb3dc4d3dfeaf0b66e47003b1634b1496deeaacf9bafe1d4097b533d3b7d30e"},"schema_version":"1.0","source":{"id":"2406.16728","kind":"arxiv","version":1}},"canonical_sha256":"ae7dd511655d056a8bb0b64f4de4db23faa9b981cf38dc27e29277c6d4eeb559","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ae7dd511655d056a8bb0b64f4de4db23faa9b981cf38dc27e29277c6d4eeb559","first_computed_at":"2026-07-05T08:36:02.921450Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:36:02.921450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0Xx3A5UeQl4hedgdJqgSteKakVWrk7DbtxgvBcRDOcYuAFaLveRj+UTAqdtccuP569jVSpzONq71RE8TFToQBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:36:02.921887Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.16728","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:27365d14b61ab54f37610e0defc01e80095263d3310139e443014725b14ebf82","sha256:1e571357e227ba3d4d441e195236a93f84977408d8c825dfcbdc52e89009fc83"],"state_sha256":"57915b76a0ba628f7c3d7c44caf00bfd85215b2a5d1baac60e6c3bbad65ed2a7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0FBKllNNV1C5bmvHDMiNBV1ni5Ab+UDXe5hKcnmp7KsYX6qDBqwX+w5wqNPtkCed/iWOkH4yt/4BEd08coLrDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T10:47:39.635359Z","bundle_sha256":"0567753b93cb540fc2376dffa768df177ee2691648ba27e62fb77c09631d972e"}}