{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:UL77CQBCGQIBIDBZIKBOJ2GDBZ","short_pith_number":"pith:UL77CQBC","canonical_record":{"source":{"id":"2307.10299","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-07-18T16:22:50Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"1f892d705edc9417301e2d17fb08a0853144eb0c644e8e6efad9f68f71a1bb76","abstract_canon_sha256":"75443addb091ab556aad4a0259f75d976bf09d55c5a570634d35875a4d2f2e5a"},"schema_version":"1.0"},"canonical_sha256":"a2fff140223410140c394282e4e8c30e7cb199a28260063252c0ab2ccdb93db0","source":{"kind":"arxiv","id":"2307.10299","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.10299","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"arxiv_version","alias_value":"2307.10299v2","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.10299","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"pith_short_12","alias_value":"UL77CQBCGQIB","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"pith_short_16","alias_value":"UL77CQBCGQIBIDBZ","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"pith_short_8","alias_value":"UL77CQBC","created_at":"2026-07-05T10:37:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:UL77CQBCGQIBIDBZIKBOJ2GDBZ","target":"record","payload":{"canonical_record":{"source":{"id":"2307.10299","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-07-18T16:22:50Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"1f892d705edc9417301e2d17fb08a0853144eb0c644e8e6efad9f68f71a1bb76","abstract_canon_sha256":"75443addb091ab556aad4a0259f75d976bf09d55c5a570634d35875a4d2f2e5a"},"schema_version":"1.0"},"canonical_sha256":"a2fff140223410140c394282e4e8c30e7cb199a28260063252c0ab2ccdb93db0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:20.705187Z","signature_b64":"6mxGrMwN1woeysVm/7/9+FxBGggOSjbReJB2C6mHTSdo6W4Pub3i6QtlGXTuIGbjQkwej4ISal3uQXzRldRODw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2fff140223410140c394282e4e8c30e7cb199a28260063252c0ab2ccdb93db0","last_reissued_at":"2026-07-05T10:37:20.704724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:20.704724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.10299","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":"2026-07-05T10:37:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DxVjCQe95/GFczfhuv4+cighK8p0O2ls0TrTMmgENYhj7kCdw/G0Z96CVQmwTC2UpUrauKdyMYlCvGUcWbCTBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T09:11:55.849756Z"},"content_sha256":"a1a4581d55b4195db27bc2f16f3f66fc2c6e11214637c04076fdeea2785228bd","schema_version":"1.0","event_id":"sha256:a1a4581d55b4195db27bc2f16f3f66fc2c6e11214637c04076fdeea2785228bd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:UL77CQBCGQIBIDBZIKBOJ2GDBZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Causality-oriented robustness: exploiting general noise interventions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"stat.ME","authors_text":"Armeen Taeb, Peter B\\\"uhlmann, Xinwei Shen","submitted_at":"2023-07-18T16:22:50Z","abstract_excerpt":"Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or distributionally robust optimization, either lack generalizability for unseen distributions or rely on postulated distance measures. Alternatively, causality offers a data-driven and structural perspective to robust predictions. However, the assumptions necessary for causal inference can be overly stringent, and the robustness offered by such causal models often lacks flexibility. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.10299","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/2307.10299/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":"2026-07-05T10:37:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6JcdqianX3KNva8nHn3JwT5dfC4QSHDqBdfd8AGRhRW9q62Fke7drx8LH2FF8+xcbAZylBf6L8QyhPS66imZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T09:11:55.850381Z"},"content_sha256":"511e9a5daaf5035a4c0985f7ccbd94663aed206be3fd4e09d2c21e83fdafb668","schema_version":"1.0","event_id":"sha256:511e9a5daaf5035a4c0985f7ccbd94663aed206be3fd4e09d2c21e83fdafb668"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UL77CQBCGQIBIDBZIKBOJ2GDBZ/bundle.json","state_url":"https://pith.science/pith/UL77CQBCGQIBIDBZIKBOJ2GDBZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UL77CQBCGQIBIDBZIKBOJ2GDBZ/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":"2026-08-09T09:11:55Z","links":{"resolver":"https://pith.science/pith/UL77CQBCGQIBIDBZIKBOJ2GDBZ","bundle":"https://pith.science/pith/UL77CQBCGQIBIDBZIKBOJ2GDBZ/bundle.json","state":"https://pith.science/pith/UL77CQBCGQIBIDBZIKBOJ2GDBZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UL77CQBCGQIBIDBZIKBOJ2GDBZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:UL77CQBCGQIBIDBZIKBOJ2GDBZ","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":"75443addb091ab556aad4a0259f75d976bf09d55c5a570634d35875a4d2f2e5a","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-07-18T16:22:50Z","title_canon_sha256":"1f892d705edc9417301e2d17fb08a0853144eb0c644e8e6efad9f68f71a1bb76"},"schema_version":"1.0","source":{"id":"2307.10299","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.10299","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"arxiv_version","alias_value":"2307.10299v2","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.10299","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"pith_short_12","alias_value":"UL77CQBCGQIB","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"pith_short_16","alias_value":"UL77CQBCGQIBIDBZ","created_at":"2026-07-05T10:37:20Z"},{"alias_kind":"pith_short_8","alias_value":"UL77CQBC","created_at":"2026-07-05T10:37:20Z"}],"graph_snapshots":[{"event_id":"sha256:511e9a5daaf5035a4c0985f7ccbd94663aed206be3fd4e09d2c21e83fdafb668","target":"graph","created_at":"2026-07-05T10:37:20Z","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/2307.10299/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or distributionally robust optimization, either lack generalizability for unseen distributions or rely on postulated distance measures. Alternatively, causality offers a data-driven and structural perspective to robust predictions. However, the assumptions necessary for causal inference can be overly stringent, and the robustness offered by such causal models often lacks flexibility. ","authors_text":"Armeen Taeb, Peter B\\\"uhlmann, Xinwei Shen","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-07-18T16:22:50Z","title":"Causality-oriented robustness: exploiting general noise interventions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.10299","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:a1a4581d55b4195db27bc2f16f3f66fc2c6e11214637c04076fdeea2785228bd","target":"record","created_at":"2026-07-05T10:37:20Z","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":"75443addb091ab556aad4a0259f75d976bf09d55c5a570634d35875a4d2f2e5a","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-07-18T16:22:50Z","title_canon_sha256":"1f892d705edc9417301e2d17fb08a0853144eb0c644e8e6efad9f68f71a1bb76"},"schema_version":"1.0","source":{"id":"2307.10299","kind":"arxiv","version":2}},"canonical_sha256":"a2fff140223410140c394282e4e8c30e7cb199a28260063252c0ab2ccdb93db0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a2fff140223410140c394282e4e8c30e7cb199a28260063252c0ab2ccdb93db0","first_computed_at":"2026-07-05T10:37:20.704724Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:37:20.704724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6mxGrMwN1woeysVm/7/9+FxBGggOSjbReJB2C6mHTSdo6W4Pub3i6QtlGXTuIGbjQkwej4ISal3uQXzRldRODw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:37:20.705187Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.10299","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a1a4581d55b4195db27bc2f16f3f66fc2c6e11214637c04076fdeea2785228bd","sha256:511e9a5daaf5035a4c0985f7ccbd94663aed206be3fd4e09d2c21e83fdafb668"],"state_sha256":"df176f1891113fc4fa643bbcfd09c879c5d70e3745cad33c5d1af6203664e76a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+ziMKrTAvHb1nqo1naswIlWokV/iGlBjjadf2KgMl3AoMtyGO5IdySl1pLpk//iwVZcfzIMPfYDpijtq1rvhBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T09:11:55.854405Z","bundle_sha256":"8b56ffa9dcd1ba5ff30db103c4db0dd069a3afb7f8dc54888e1ba8d00af98a75"}}