{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CKIXY5L2QBWNODBO77LI35Y6MY","short_pith_number":"pith:CKIXY5L2","canonical_record":{"source":{"id":"2405.08498","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-14T10:55:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a3b226545f7b7a6e159ffe5e5a23b7c1a000c42bc235bb8a4593956ffbf11bd2","abstract_canon_sha256":"4aedefeb9c3dcc3595b3a5214a487cb39f2c79644ba579a90adbe3cd7160bd4a"},"schema_version":"1.0"},"canonical_sha256":"12917c757a806cd70c2effd68df71e661d453ff5a8decaaed8fd19ec2e4c96dd","source":{"kind":"arxiv","id":"2405.08498","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.08498","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"arxiv_version","alias_value":"2405.08498v3","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.08498","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"pith_short_12","alias_value":"CKIXY5L2QBWN","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"pith_short_16","alias_value":"CKIXY5L2QBWNODBO","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"pith_short_8","alias_value":"CKIXY5L2","created_at":"2026-07-05T11:25:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CKIXY5L2QBWNODBO77LI35Y6MY","target":"record","payload":{"canonical_record":{"source":{"id":"2405.08498","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-14T10:55:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a3b226545f7b7a6e159ffe5e5a23b7c1a000c42bc235bb8a4593956ffbf11bd2","abstract_canon_sha256":"4aedefeb9c3dcc3595b3a5214a487cb39f2c79644ba579a90adbe3cd7160bd4a"},"schema_version":"1.0"},"canonical_sha256":"12917c757a806cd70c2effd68df71e661d453ff5a8decaaed8fd19ec2e4c96dd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:59.710896Z","signature_b64":"VdH7q/zam/Ea8Rf3O6n7bBThIT/kgiNF1xJ08rhYyketNmhceFrKNNoUkw5dq1aUx66Y7hxiRpX1pnbciwysBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12917c757a806cd70c2effd68df71e661d453ff5a8decaaed8fd19ec2e4c96dd","last_reissued_at":"2026-07-05T11:25:59.710459Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:59.710459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.08498","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:25:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"28An8BOooNtE8UmauBQi94kvzLrYrxU9kod2+8CVYGNbk16fGzGrCxeOeKeNFavsInpSFQ+syvIbF7cjfuHQDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T15:02:11.005028Z"},"content_sha256":"fd3b694b5414f9a0d0d50f08ed92dd735553897672afb2187daa9fb58554de2f","schema_version":"1.0","event_id":"sha256:fd3b694b5414f9a0d0d50f08ed92dd735553897672afb2187daa9fb58554de2f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CKIXY5L2QBWNODBO77LI35Y6MY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Decision Policies with Instrumental Variables through Double Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ashkan Soleymani, Daqian Shao, Francesco Quinzan, Marta Kwiatkowska","submitted_at":"2024-05-14T10:55:04Z","abstract_excerpt":"A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable known as the instrument, is a standard technique for learning causal relationships between confounded action, outcome, and context variables. Most recent IV regression algorithms use a two-stage approach, where a deep neural network (DNN) estimator learnt in the first stage is directly plugged into the second stage, in which another DNN is used to es"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.08498","kind":"arxiv","version":3},"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/2405.08498/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":"2026-07-05T11:25:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ate2t/0Sa+tznPjElPDAOkw8sQPCSgsjGv1EsIW/h+QkG+mCSUU5a9BniSC365YaajrTIrNbJAoiWxfRtgaxDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T15:02:11.005422Z"},"content_sha256":"e6d5c185ffc60208f64fc55f4cc2fa020a6ee7ed360b419419c29efc58bad1cb","schema_version":"1.0","event_id":"sha256:e6d5c185ffc60208f64fc55f4cc2fa020a6ee7ed360b419419c29efc58bad1cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CKIXY5L2QBWNODBO77LI35Y6MY/bundle.json","state_url":"https://pith.science/pith/CKIXY5L2QBWNODBO77LI35Y6MY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CKIXY5L2QBWNODBO77LI35Y6MY/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":"2026-07-06T15:02:11Z","links":{"resolver":"https://pith.science/pith/CKIXY5L2QBWNODBO77LI35Y6MY","bundle":"https://pith.science/pith/CKIXY5L2QBWNODBO77LI35Y6MY/bundle.json","state":"https://pith.science/pith/CKIXY5L2QBWNODBO77LI35Y6MY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CKIXY5L2QBWNODBO77LI35Y6MY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CKIXY5L2QBWNODBO77LI35Y6MY","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":"4aedefeb9c3dcc3595b3a5214a487cb39f2c79644ba579a90adbe3cd7160bd4a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-14T10:55:04Z","title_canon_sha256":"a3b226545f7b7a6e159ffe5e5a23b7c1a000c42bc235bb8a4593956ffbf11bd2"},"schema_version":"1.0","source":{"id":"2405.08498","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.08498","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"arxiv_version","alias_value":"2405.08498v3","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.08498","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"pith_short_12","alias_value":"CKIXY5L2QBWN","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"pith_short_16","alias_value":"CKIXY5L2QBWNODBO","created_at":"2026-07-05T11:25:59Z"},{"alias_kind":"pith_short_8","alias_value":"CKIXY5L2","created_at":"2026-07-05T11:25:59Z"}],"graph_snapshots":[{"event_id":"sha256:e6d5c185ffc60208f64fc55f4cc2fa020a6ee7ed360b419419c29efc58bad1cb","target":"graph","created_at":"2026-07-05T11:25:59Z","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/2405.08498/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable known as the instrument, is a standard technique for learning causal relationships between confounded action, outcome, and context variables. Most recent IV regression algorithms use a two-stage approach, where a deep neural network (DNN) estimator learnt in the first stage is directly plugged into the second stage, in which another DNN is used to es","authors_text":"Ashkan Soleymani, Daqian Shao, Francesco Quinzan, Marta Kwiatkowska","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-14T10:55:04Z","title":"Learning Decision Policies with Instrumental Variables through Double Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.08498","kind":"arxiv","version":3},"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:fd3b694b5414f9a0d0d50f08ed92dd735553897672afb2187daa9fb58554de2f","target":"record","created_at":"2026-07-05T11:25:59Z","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":"4aedefeb9c3dcc3595b3a5214a487cb39f2c79644ba579a90adbe3cd7160bd4a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-14T10:55:04Z","title_canon_sha256":"a3b226545f7b7a6e159ffe5e5a23b7c1a000c42bc235bb8a4593956ffbf11bd2"},"schema_version":"1.0","source":{"id":"2405.08498","kind":"arxiv","version":3}},"canonical_sha256":"12917c757a806cd70c2effd68df71e661d453ff5a8decaaed8fd19ec2e4c96dd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"12917c757a806cd70c2effd68df71e661d453ff5a8decaaed8fd19ec2e4c96dd","first_computed_at":"2026-07-05T11:25:59.710459Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:59.710459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VdH7q/zam/Ea8Rf3O6n7bBThIT/kgiNF1xJ08rhYyketNmhceFrKNNoUkw5dq1aUx66Y7hxiRpX1pnbciwysBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:59.710896Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.08498","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd3b694b5414f9a0d0d50f08ed92dd735553897672afb2187daa9fb58554de2f","sha256:e6d5c185ffc60208f64fc55f4cc2fa020a6ee7ed360b419419c29efc58bad1cb"],"state_sha256":"e8e5967f585ab39bf7e7546292ad86f065240da11232ba7b28d65ed6a3aed6d3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cUT8zYpFCRs/9CAHHv88c4fruLClvLH5MVomXdG3TKo07VTIenojxHiGd2CgO+3OYVru6QUm1nhzmFUH5j0tCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T15:02:11.007427Z","bundle_sha256":"5f9a501ae65da95e172978201a94d7a58623683d4854fdedcecf7736e0c11fa1"}}