{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:6UJ4OZBZIXZAXKCGNKPJX7OKYA","short_pith_number":"pith:6UJ4OZBZ","canonical_record":{"source":{"id":"2604.10845","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2026-04-12T22:35:04Z","cross_cats_sorted":["econ.EM"],"title_canon_sha256":"dcd6d07b75ca7a7e0d8a08f39ca341d8c7ef3854813863a1e3d69e6d6199ddad","abstract_canon_sha256":"aafd88124a2513206e8c5d6f7987e47577865866c671acf928aa5520f57a0a2e"},"schema_version":"1.0"},"canonical_sha256":"f513c7643945f20ba8466a9e9bfdcac01f43b1352856769216428143c7bd9ebf","source":{"kind":"arxiv","id":"2604.10845","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.10845","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"arxiv_version","alias_value":"2604.10845v2","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10845","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"pith_short_12","alias_value":"6UJ4OZBZIXZA","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"pith_short_16","alias_value":"6UJ4OZBZIXZAXKCG","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"pith_short_8","alias_value":"6UJ4OZBZ","created_at":"2026-05-26T02:05:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:6UJ4OZBZIXZAXKCGNKPJX7OKYA","target":"record","payload":{"canonical_record":{"source":{"id":"2604.10845","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2026-04-12T22:35:04Z","cross_cats_sorted":["econ.EM"],"title_canon_sha256":"dcd6d07b75ca7a7e0d8a08f39ca341d8c7ef3854813863a1e3d69e6d6199ddad","abstract_canon_sha256":"aafd88124a2513206e8c5d6f7987e47577865866c671acf928aa5520f57a0a2e"},"schema_version":"1.0"},"canonical_sha256":"f513c7643945f20ba8466a9e9bfdcac01f43b1352856769216428143c7bd9ebf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:05:09.261270Z","signature_b64":"JyKZKhooucRh9w0nmP3hWC7VCfem3EW4WUjm+LF56SV1QaDj4TV/RR8pg9tXV+KsWMiaDUz0A1jwljjNNlRRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f513c7643945f20ba8466a9e9bfdcac01f43b1352856769216428143c7bd9ebf","last_reissued_at":"2026-05-26T02:05:09.260459Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:05:09.260459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.10845","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T02:05:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DVRWC5yPpnGDdbi6swlAmh0b2L4IcUqHULMMyHoC1m1lAA91cDtUPZlsbKzJEcKB/phdY8FkAaLc1vdetSJIBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T06:52:26.676874Z"},"content_sha256":"79fdbc1b109e320dff1e55227bbe7f5fcb29217aea5d555d9136d4a26f57d069","schema_version":"1.0","event_id":"sha256:79fdbc1b109e320dff1e55227bbe7f5fcb29217aea5d555d9136d4a26f57d069"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:6UJ4OZBZIXZAXKCGNKPJX7OKYA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Preferences from Conjoint Data: A Structural Deep Learning Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"Embedding a deep neural network inside a random utility logit model recovers flexible preference heterogeneity from conjoint data.","cross_cats":["econ.EM"],"primary_cat":"stat.ME","authors_text":"Avidit Acharya, Jens Hainmueller, Yiqing Xu","submitted_at":"2026-04-12T22:35:04Z","abstract_excerpt":"Conjoint experiments randomize multidimensional profiles, offering a powerful design for recovering structural preference parameters -- including marginal rates of substitution, willingness to pay, and the distribution of preferences across a population. Yet the dominant approach in political science has focused on nonparametric causal estimands that do not leverage this potential. We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. The neural n"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. [...] We apply our method to three prominent conjoint studies and find rich preference heterogeneity masked by reduced-form averages: a near-zero gender effect coexists with 83% preferring female candidates, opposition to undemocratic behavior is near-universal but varies sharply in intensity, and progressive tax preferences cut across every partisan subgroup.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the random utility logit model with neural network-embedded parameters accurately represents the choice process, and that double/debiased machine learning successfully debiases the estimates despite the high flexibility of the neural network.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A structural deep learning approach for conjoint data reveals rich preference heterogeneity masked by reduced-form averages in three studies.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Embedding a deep neural network inside a random utility logit model recovers flexible preference heterogeneity from conjoint data.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ef365248b953a0a6ee9f458b458ce19c4cb1dc8880704e8e2857c394e7ed283d"},"source":{"id":"2604.10845","kind":"arxiv","version":2},"verdict":{"id":"1a7ab9ad-4288-49a2-91c0-bd81421b4940","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T15:05:00.691263Z","strongest_claim":"We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. [...] We apply our method to three prominent conjoint studies and find rich preference heterogeneity masked by reduced-form averages: a near-zero gender effect coexists with 83% preferring female candidates, opposition to undemocratic behavior is near-universal but varies sharply in intensity, and progressive tax preferences cut across every partisan subgroup.","one_line_summary":"A structural deep learning approach for conjoint data reveals rich preference heterogeneity masked by reduced-form averages in three studies.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that the random utility logit model with neural network-embedded parameters accurately represents the choice process, and that double/debiased machine learning successfully debiases the estimates despite the high flexibility of the neural network.","pith_extraction_headline":"Embedding a deep neural network inside a random utility logit model recovers flexible preference heterogeneity from conjoint data."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.10845/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":"1a7ab9ad-4288-49a2-91c0-bd81421b4940"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T02:05:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bHLmaV0pZZxy19o6LcTycRz0zYDiCw1J9lSJHUkgxi1Gjharml0BrF2zy2Ukp/fGwSJNYYOxpqBXjbejNzmQDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T06:52:26.677653Z"},"content_sha256":"b618c8013152f2e25684d782e020cb9a5e034ea882156c7bbc4efa1fa7fd03ce","schema_version":"1.0","event_id":"sha256:b618c8013152f2e25684d782e020cb9a5e034ea882156c7bbc4efa1fa7fd03ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6UJ4OZBZIXZAXKCGNKPJX7OKYA/bundle.json","state_url":"https://pith.science/pith/6UJ4OZBZIXZAXKCGNKPJX7OKYA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6UJ4OZBZIXZAXKCGNKPJX7OKYA/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":"2026-06-01T06:52:26Z","links":{"resolver":"https://pith.science/pith/6UJ4OZBZIXZAXKCGNKPJX7OKYA","bundle":"https://pith.science/pith/6UJ4OZBZIXZAXKCGNKPJX7OKYA/bundle.json","state":"https://pith.science/pith/6UJ4OZBZIXZAXKCGNKPJX7OKYA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6UJ4OZBZIXZAXKCGNKPJX7OKYA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6UJ4OZBZIXZAXKCGNKPJX7OKYA","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":"aafd88124a2513206e8c5d6f7987e47577865866c671acf928aa5520f57a0a2e","cross_cats_sorted":["econ.EM"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2026-04-12T22:35:04Z","title_canon_sha256":"dcd6d07b75ca7a7e0d8a08f39ca341d8c7ef3854813863a1e3d69e6d6199ddad"},"schema_version":"1.0","source":{"id":"2604.10845","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.10845","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"arxiv_version","alias_value":"2604.10845v2","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10845","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"pith_short_12","alias_value":"6UJ4OZBZIXZA","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"pith_short_16","alias_value":"6UJ4OZBZIXZAXKCG","created_at":"2026-05-26T02:05:09Z"},{"alias_kind":"pith_short_8","alias_value":"6UJ4OZBZ","created_at":"2026-05-26T02:05:09Z"}],"graph_snapshots":[{"event_id":"sha256:b618c8013152f2e25684d782e020cb9a5e034ea882156c7bbc4efa1fa7fd03ce","target":"graph","created_at":"2026-05-26T02:05:09Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. [...] We apply our method to three prominent conjoint studies and find rich preference heterogeneity masked by reduced-form averages: a near-zero gender effect coexists with 83% preferring female candidates, opposition to undemocratic behavior is near-universal but varies sharply in intensity, and progressive tax preferences cut across every partisan subgroup."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that the random utility logit model with neural network-embedded parameters accurately represents the choice process, and that double/debiased machine learning successfully debiases the estimates despite the high flexibility of the neural network."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A structural deep learning approach for conjoint data reveals rich preference heterogeneity masked by reduced-form averages in three studies."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Embedding a deep neural network inside a random utility logit model recovers flexible preference heterogeneity from conjoint data."}],"snapshot_sha256":"ef365248b953a0a6ee9f458b458ce19c4cb1dc8880704e8e2857c394e7ed283d"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.10845/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Conjoint experiments randomize multidimensional profiles, offering a powerful design for recovering structural preference parameters -- including marginal rates of substitution, willingness to pay, and the distribution of preferences across a population. Yet the dominant approach in political science has focused on nonparametric causal estimands that do not leverage this potential. We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. The neural n","authors_text":"Avidit Acharya, Jens Hainmueller, Yiqing Xu","cross_cats":["econ.EM"],"headline":"Embedding a deep neural network inside a random utility logit model recovers flexible preference heterogeneity from conjoint data.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2026-04-12T22:35:04Z","title":"Learning Preferences from Conjoint Data: A Structural Deep Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.10845","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T15:05:00.691263Z","id":"1a7ab9ad-4288-49a2-91c0-bd81421b4940","model_set":{"reader":"grok-4.3"},"one_line_summary":"A structural deep learning approach for conjoint data reveals rich preference heterogeneity masked by reduced-form averages in three studies.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Embedding a deep neural network inside a random utility logit model recovers flexible preference heterogeneity from conjoint data.","strongest_claim":"We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. [...] We apply our method to three prominent conjoint studies and find rich preference heterogeneity masked by reduced-form averages: a near-zero gender effect coexists with 83% preferring female candidates, opposition to undemocratic behavior is near-universal but varies sharply in intensity, and progressive tax preferences cut across every partisan subgroup.","weakest_assumption":"The assumption that the random utility logit model with neural network-embedded parameters accurately represents the choice process, and that double/debiased machine learning successfully debiases the estimates despite the high flexibility of the neural network."}},"verdict_id":"1a7ab9ad-4288-49a2-91c0-bd81421b4940"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:79fdbc1b109e320dff1e55227bbe7f5fcb29217aea5d555d9136d4a26f57d069","target":"record","created_at":"2026-05-26T02:05:09Z","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":"aafd88124a2513206e8c5d6f7987e47577865866c671acf928aa5520f57a0a2e","cross_cats_sorted":["econ.EM"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2026-04-12T22:35:04Z","title_canon_sha256":"dcd6d07b75ca7a7e0d8a08f39ca341d8c7ef3854813863a1e3d69e6d6199ddad"},"schema_version":"1.0","source":{"id":"2604.10845","kind":"arxiv","version":2}},"canonical_sha256":"f513c7643945f20ba8466a9e9bfdcac01f43b1352856769216428143c7bd9ebf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f513c7643945f20ba8466a9e9bfdcac01f43b1352856769216428143c7bd9ebf","first_computed_at":"2026-05-26T02:05:09.260459Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:05:09.260459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JyKZKhooucRh9w0nmP3hWC7VCfem3EW4WUjm+LF56SV1QaDj4TV/RR8pg9tXV+KsWMiaDUz0A1jwljjNNlRRDQ==","signature_status":"signed_v1","signed_at":"2026-05-26T02:05:09.261270Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.10845","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79fdbc1b109e320dff1e55227bbe7f5fcb29217aea5d555d9136d4a26f57d069","sha256:b618c8013152f2e25684d782e020cb9a5e034ea882156c7bbc4efa1fa7fd03ce"],"state_sha256":"ccc9f208c997947fa0a0217a3ccea5bfce9df665226ac0373c7a1d985b82dcbf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J5oL2gaqzEx+vKDHroiVGJdEqhZpKUIxOs78MYST9uVNe4xv9AIPhS40v7GZD879ZD5HIqlmbJD71DskdRmAAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T06:52:26.681537Z","bundle_sha256":"fb27b1cf434e8d09d9967528888aa871b0a2d34cbb88390f546bee13a9e2f5c6"}}