{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:GZ43M256G7CKJX5BSXE5I2IX34","short_pith_number":"pith:GZ43M256","canonical_record":{"source":{"id":"2011.05466","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T00:01:14Z","cross_cats_sorted":[],"title_canon_sha256":"ed63d33de3f44710149e76f36a5b34e41627857b68d3744802fe0ede97e7244a","abstract_canon_sha256":"756e956f3f97242d0fce38e6255c4140efa56e9915d35f6312d39a8c6942965c"},"schema_version":"1.0"},"canonical_sha256":"3679b66bbe37c4a4dfa195c9d46917df1fba7fa5a7c0ca991dfb0938e67e12a9","source":{"kind":"arxiv","id":"2011.05466","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.05466","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"arxiv_version","alias_value":"2011.05466v2","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.05466","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"pith_short_12","alias_value":"GZ43M256G7CK","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"pith_short_16","alias_value":"GZ43M256G7CKJX5B","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"pith_short_8","alias_value":"GZ43M256","created_at":"2026-07-05T05:10:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:GZ43M256G7CKJX5BSXE5I2IX34","target":"record","payload":{"canonical_record":{"source":{"id":"2011.05466","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T00:01:14Z","cross_cats_sorted":[],"title_canon_sha256":"ed63d33de3f44710149e76f36a5b34e41627857b68d3744802fe0ede97e7244a","abstract_canon_sha256":"756e956f3f97242d0fce38e6255c4140efa56e9915d35f6312d39a8c6942965c"},"schema_version":"1.0"},"canonical_sha256":"3679b66bbe37c4a4dfa195c9d46917df1fba7fa5a7c0ca991dfb0938e67e12a9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:10:51.912679Z","signature_b64":"o4EA+sNh/CkvXJiC9OCg6gAGFPk40rfuNU0LHf6/H494721hfiYfoOxzoP1q3vjCCIb83Fa56UR5FVPMmJQ9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3679b66bbe37c4a4dfa195c9d46917df1fba7fa5a7c0ca991dfb0938e67e12a9","last_reissued_at":"2026-07-05T05:10:51.912118Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:10:51.912118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.05466","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":"2026-07-05T05:10:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tid1+YZoXWk8OMqWz9ReNlfLZupLye7FR86312Lx6DxA5d7NVIbGtSO4t3ywypPR65FVlpvDP8aCI17yqef+CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:30:21.008790Z"},"content_sha256":"c143c1a7c32ce8840c32d245f91decb90c397a2976038f5f29a94da9f1c5266f","schema_version":"1.0","event_id":"sha256:c143c1a7c32ce8840c32d245f91decb90c397a2976038f5f29a94da9f1c5266f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:GZ43M256G7CKJX5BSXE5I2IX34","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Incorporating Causal Effects into Deep Learning Predictions on EHR Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gyorgy Simon, Haoyu Yang, Jia Li, Michael Steinbach, Vipin Kumar, Xiaowei Jia","submitted_at":"2020-11-11T00:01:14Z","abstract_excerpt":"Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality. Because of its highly complex underlying causality and limited observable nature, causal inference on EHR is quite challenging. Deep Learning (DL) achieved great success among the advanced machine learning methodologies. Nevertheless, it is still obstructed by the inappropriately assumed causal conditions. This work proposed a novel method to quantify clinically well-defined causal effects as a generalized estimation vector that is simply utilizable for causal models. We incorporated it into DL mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.05466","kind":"arxiv","version":2},"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/2011.05466/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":"2026-07-05T05:10:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H/HHLRw4MjmGk/PcdOk7v5rmjlseGLc2r92yYI05+aAxsW1eRNo78zcyGxxqTWpQha3wBW3Pe3LXTvNww7fzBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:30:21.009439Z"},"content_sha256":"cbdd5e979af1302c70222131db793cf095ca8d67fe8b29b813b0f96bbba1398a","schema_version":"1.0","event_id":"sha256:cbdd5e979af1302c70222131db793cf095ca8d67fe8b29b813b0f96bbba1398a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GZ43M256G7CKJX5BSXE5I2IX34/bundle.json","state_url":"https://pith.science/pith/GZ43M256G7CKJX5BSXE5I2IX34/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GZ43M256G7CKJX5BSXE5I2IX34/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":"2026-08-03T16:30:21Z","links":{"resolver":"https://pith.science/pith/GZ43M256G7CKJX5BSXE5I2IX34","bundle":"https://pith.science/pith/GZ43M256G7CKJX5BSXE5I2IX34/bundle.json","state":"https://pith.science/pith/GZ43M256G7CKJX5BSXE5I2IX34/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GZ43M256G7CKJX5BSXE5I2IX34/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:GZ43M256G7CKJX5BSXE5I2IX34","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":"756e956f3f97242d0fce38e6255c4140efa56e9915d35f6312d39a8c6942965c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T00:01:14Z","title_canon_sha256":"ed63d33de3f44710149e76f36a5b34e41627857b68d3744802fe0ede97e7244a"},"schema_version":"1.0","source":{"id":"2011.05466","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.05466","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"arxiv_version","alias_value":"2011.05466v2","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.05466","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"pith_short_12","alias_value":"GZ43M256G7CK","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"pith_short_16","alias_value":"GZ43M256G7CKJX5B","created_at":"2026-07-05T05:10:51Z"},{"alias_kind":"pith_short_8","alias_value":"GZ43M256","created_at":"2026-07-05T05:10:51Z"}],"graph_snapshots":[{"event_id":"sha256:cbdd5e979af1302c70222131db793cf095ca8d67fe8b29b813b0f96bbba1398a","target":"graph","created_at":"2026-07-05T05:10:51Z","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/2011.05466/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality. Because of its highly complex underlying causality and limited observable nature, causal inference on EHR is quite challenging. Deep Learning (DL) achieved great success among the advanced machine learning methodologies. Nevertheless, it is still obstructed by the inappropriately assumed causal conditions. This work proposed a novel method to quantify clinically well-defined causal effects as a generalized estimation vector that is simply utilizable for causal models. We incorporated it into DL mod","authors_text":"Gyorgy Simon, Haoyu Yang, Jia Li, Michael Steinbach, Vipin Kumar, Xiaowei Jia","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T00:01:14Z","title":"Incorporating Causal Effects into Deep Learning Predictions on EHR Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.05466","kind":"arxiv","version":2},"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:c143c1a7c32ce8840c32d245f91decb90c397a2976038f5f29a94da9f1c5266f","target":"record","created_at":"2026-07-05T05:10:51Z","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":"756e956f3f97242d0fce38e6255c4140efa56e9915d35f6312d39a8c6942965c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T00:01:14Z","title_canon_sha256":"ed63d33de3f44710149e76f36a5b34e41627857b68d3744802fe0ede97e7244a"},"schema_version":"1.0","source":{"id":"2011.05466","kind":"arxiv","version":2}},"canonical_sha256":"3679b66bbe37c4a4dfa195c9d46917df1fba7fa5a7c0ca991dfb0938e67e12a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3679b66bbe37c4a4dfa195c9d46917df1fba7fa5a7c0ca991dfb0938e67e12a9","first_computed_at":"2026-07-05T05:10:51.912118Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:10:51.912118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"o4EA+sNh/CkvXJiC9OCg6gAGFPk40rfuNU0LHf6/H494721hfiYfoOxzoP1q3vjCCIb83Fa56UR5FVPMmJQ9Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:10:51.912679Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.05466","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c143c1a7c32ce8840c32d245f91decb90c397a2976038f5f29a94da9f1c5266f","sha256:cbdd5e979af1302c70222131db793cf095ca8d67fe8b29b813b0f96bbba1398a"],"state_sha256":"a26ba41b513e726b76ce63573ebc00dc816ad7501670c2a565daf8fe978675d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AsiHwUQUHCENefP0dvJT3jepctG3y9N0jV/7za/l1D+oAzCET++qr20to6UOw9jmV8lNMFE+JG0UarJONxV3Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:30:21.015327Z","bundle_sha256":"b428bd0e74d5f422074acf7a913ace48fe81fbf07998e01528eb9f7e78bfc574"}}