{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:UVZWDUSOUVWLXN5IWH3ONEBLHM","short_pith_number":"pith:UVZWDUSO","canonical_record":{"source":{"id":"2002.12626","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-28T10:02:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"882a920b25670e5751c4606ea249389f0b856235ce7571ed1be4cdd23c335295","abstract_canon_sha256":"3b0dd9b9b8ad19b6cecbf780317be5e91605b8a820809af72ebffac08f531f4e"},"schema_version":"1.0"},"canonical_sha256":"a57361d24ea56cbbb7a8b1f6e6902b3b16cbcd6a854f67df0a4af9307a917a6c","source":{"kind":"arxiv","id":"2002.12626","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.12626","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"arxiv_version","alias_value":"2002.12626v1","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.12626","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"pith_short_12","alias_value":"UVZWDUSOUVWL","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"pith_short_16","alias_value":"UVZWDUSOUVWLXN5I","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"pith_short_8","alias_value":"UVZWDUSO","created_at":"2026-07-05T00:44:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:UVZWDUSOUVWLXN5IWH3ONEBLHM","target":"record","payload":{"canonical_record":{"source":{"id":"2002.12626","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-28T10:02:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"882a920b25670e5751c4606ea249389f0b856235ce7571ed1be4cdd23c335295","abstract_canon_sha256":"3b0dd9b9b8ad19b6cecbf780317be5e91605b8a820809af72ebffac08f531f4e"},"schema_version":"1.0"},"canonical_sha256":"a57361d24ea56cbbb7a8b1f6e6902b3b16cbcd6a854f67df0a4af9307a917a6c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:44:32.218847Z","signature_b64":"zUuv8OHI7gexTVgHkDkrAqpFkSjFmUNFgsMDrKg8o0UBfCv1ZF4NgZxS2x/9HXo0lCzDilSLQlYhn0pJ5rybBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a57361d24ea56cbbb7a8b1f6e6902b3b16cbcd6a854f67df0a4af9307a917a6c","last_reissued_at":"2026-07-05T00:44:32.218488Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:44:32.218488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.12626","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":"2026-07-05T00:44:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IpoKkJhogcGZNqvf/QzFGO5RmAki0nWampndGzdJ3iijN+qOpicChD5qruyGZIpiC6gLdDyPOL6ZjjwiQON4BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T11:54:05.239290Z"},"content_sha256":"926db6417b9ffdc83b871677fc8dcf86675610c48ae393f16cbed4bf5993dbd8","schema_version":"1.0","event_id":"sha256:926db6417b9ffdc83b871677fc8dcf86675610c48ae393f16cbed4bf5993dbd8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:UVZWDUSOUVWLXN5IWH3ONEBLHM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Causality and Robust Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Akihiro Yabe","submitted_at":"2020-02-28T10:02:59Z","abstract_excerpt":"A decision-maker must consider cofounding bias when attempting to apply machine learning prediction, and, while feature selection is widely recognized as important process in data-analysis, it could cause cofounding bias. A causal Bayesian network is a standard tool for describing causal relationships, and if relationships are known, then adjustment criteria can determine with which features cofounding bias disappears. A standard modification would thus utilize causal discovery algorithms for preventing cofounding bias in feature selection. Causal discovery algorithms, however, essentially rel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.12626","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/2002.12626/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":"2026-07-05T00:44:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CK6+jJLE2IBxzSvZIchW3213Nb+LhwxtJFbezgWw5g6KkDWCrk/bDwOIccPBaiKruxO/GKIvYaUghQ33inJ9DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T11:54:05.240160Z"},"content_sha256":"8e91b185cfeed91e5fc8ecef52c66ddfce772786cdc13d2d2f6b4a15a0d5e1ca","schema_version":"1.0","event_id":"sha256:8e91b185cfeed91e5fc8ecef52c66ddfce772786cdc13d2d2f6b4a15a0d5e1ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UVZWDUSOUVWLXN5IWH3ONEBLHM/bundle.json","state_url":"https://pith.science/pith/UVZWDUSOUVWLXN5IWH3ONEBLHM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UVZWDUSOUVWLXN5IWH3ONEBLHM/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":"2026-08-04T11:54:05Z","links":{"resolver":"https://pith.science/pith/UVZWDUSOUVWLXN5IWH3ONEBLHM","bundle":"https://pith.science/pith/UVZWDUSOUVWLXN5IWH3ONEBLHM/bundle.json","state":"https://pith.science/pith/UVZWDUSOUVWLXN5IWH3ONEBLHM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UVZWDUSOUVWLXN5IWH3ONEBLHM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:UVZWDUSOUVWLXN5IWH3ONEBLHM","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":"3b0dd9b9b8ad19b6cecbf780317be5e91605b8a820809af72ebffac08f531f4e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-28T10:02:59Z","title_canon_sha256":"882a920b25670e5751c4606ea249389f0b856235ce7571ed1be4cdd23c335295"},"schema_version":"1.0","source":{"id":"2002.12626","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.12626","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"arxiv_version","alias_value":"2002.12626v1","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.12626","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"pith_short_12","alias_value":"UVZWDUSOUVWL","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"pith_short_16","alias_value":"UVZWDUSOUVWLXN5I","created_at":"2026-07-05T00:44:32Z"},{"alias_kind":"pith_short_8","alias_value":"UVZWDUSO","created_at":"2026-07-05T00:44:32Z"}],"graph_snapshots":[{"event_id":"sha256:8e91b185cfeed91e5fc8ecef52c66ddfce772786cdc13d2d2f6b4a15a0d5e1ca","target":"graph","created_at":"2026-07-05T00:44:32Z","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/2002.12626/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A decision-maker must consider cofounding bias when attempting to apply machine learning prediction, and, while feature selection is widely recognized as important process in data-analysis, it could cause cofounding bias. A causal Bayesian network is a standard tool for describing causal relationships, and if relationships are known, then adjustment criteria can determine with which features cofounding bias disappears. A standard modification would thus utilize causal discovery algorithms for preventing cofounding bias in feature selection. Causal discovery algorithms, however, essentially rel","authors_text":"Akihiro Yabe","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-28T10:02:59Z","title":"Causality and Robust Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.12626","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":"sha256:926db6417b9ffdc83b871677fc8dcf86675610c48ae393f16cbed4bf5993dbd8","target":"record","created_at":"2026-07-05T00:44:32Z","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":"3b0dd9b9b8ad19b6cecbf780317be5e91605b8a820809af72ebffac08f531f4e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-28T10:02:59Z","title_canon_sha256":"882a920b25670e5751c4606ea249389f0b856235ce7571ed1be4cdd23c335295"},"schema_version":"1.0","source":{"id":"2002.12626","kind":"arxiv","version":1}},"canonical_sha256":"a57361d24ea56cbbb7a8b1f6e6902b3b16cbcd6a854f67df0a4af9307a917a6c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a57361d24ea56cbbb7a8b1f6e6902b3b16cbcd6a854f67df0a4af9307a917a6c","first_computed_at":"2026-07-05T00:44:32.218488Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:44:32.218488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zUuv8OHI7gexTVgHkDkrAqpFkSjFmUNFgsMDrKg8o0UBfCv1ZF4NgZxS2x/9HXo0lCzDilSLQlYhn0pJ5rybBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:44:32.218847Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.12626","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:926db6417b9ffdc83b871677fc8dcf86675610c48ae393f16cbed4bf5993dbd8","sha256:8e91b185cfeed91e5fc8ecef52c66ddfce772786cdc13d2d2f6b4a15a0d5e1ca"],"state_sha256":"53ba88315eb843785e012aad69365a491bd55908d9f5a406a31f0166dc020900"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"67qgzljSL/ub69zLL5e2NuWmIks1NVxQN1E1C+gRMz0bzSHYXd2/mCXXhFYc1EK934aUDnudQrqJT6XWZpFWDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T11:54:05.246644Z","bundle_sha256":"237e95956ce12c5c8bf6490be38d7caa3c25a5292889e88d6065ec20b8bd76d1"}}