{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:4D6HFNJG3GTVB5FZSABRWKIILL","short_pith_number":"pith:4D6HFNJG","canonical_record":{"source":{"id":"1706.08058","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-06-25T08:25:25Z","cross_cats_sorted":["stat.AP","stat.ME","stat.TH"],"title_canon_sha256":"e1ff778fa91943973abd58db906fd6a2e58e4c7a06ba219ef7f64a2a23a390e6","abstract_canon_sha256":"73a948127b018be345b33f3fbb4db53973f994db06495477c3d31ab1942160b2"},"schema_version":"1.0"},"canonical_sha256":"e0fc72b526d9a750f4b990031b29085ac5bf1e9a43d50a95aefb3e439048192e","source":{"kind":"arxiv","id":"1706.08058","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.08058","created_at":"2026-05-18T00:14:56Z"},{"alias_kind":"arxiv_version","alias_value":"1706.08058v2","created_at":"2026-05-18T00:14:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.08058","created_at":"2026-05-18T00:14:56Z"},{"alias_kind":"pith_short_12","alias_value":"4D6HFNJG3GTV","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"4D6HFNJG3GTVB5FZ","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"4D6HFNJG","created_at":"2026-05-18T12:30:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:4D6HFNJG3GTVB5FZSABRWKIILL","target":"record","payload":{"canonical_record":{"source":{"id":"1706.08058","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-06-25T08:25:25Z","cross_cats_sorted":["stat.AP","stat.ME","stat.TH"],"title_canon_sha256":"e1ff778fa91943973abd58db906fd6a2e58e4c7a06ba219ef7f64a2a23a390e6","abstract_canon_sha256":"73a948127b018be345b33f3fbb4db53973f994db06495477c3d31ab1942160b2"},"schema_version":"1.0"},"canonical_sha256":"e0fc72b526d9a750f4b990031b29085ac5bf1e9a43d50a95aefb3e439048192e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:56.906752Z","signature_b64":"p68iT20g8xJ+y9QJWKijVP8ZjB1yvOKrhwzNnQFYLxXATabCX2hjCfbknrWcMDm90uZigMPgHhAGkTFuybnnCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0fc72b526d9a750f4b990031b29085ac5bf1e9a43d50a95aefb3e439048192e","last_reissued_at":"2026-05-18T00:14:56.906133Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:56.906133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.08058","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":"2026-05-18T00:14:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4Ruw7MWEIWunv+aqhsgL2kkplnhtNU/WlQzgCOENA4VbdUrYxMq67VUg6cbkd8tzx3mK78TMWaVzxk8CAwzKDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T23:05:11.119996Z"},"content_sha256":"0b8dd016f6653a2d6e9f5c6a1ac352a5790f9c1e9d564dc1fee6349a112736eb","schema_version":"1.0","event_id":"sha256:0b8dd016f6653a2d6e9f5c6a1ac352a5790f9c1e9d564dc1fee6349a112736eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:4D6HFNJG3GTVB5FZSABRWKIILL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Invariant Causal Prediction for Sequential Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Jonas Peters, Niklas Pfister, Peter B\\\"uhlmann","submitted_at":"2017-06-25T08:25:25Z","abstract_excerpt":"We investigate the problem of inferring the causal predictors of a response $Y$ from a set of $d$ explanatory variables $(X^1,\\dots,X^d)$. Classical ordinary least squares regression includes all predictors that reduce the variance of $Y$. Using only the causal predictors instead leads to models that have the advantage of remaining invariant under interventions, loosely speaking they lead to invariance across different \"environments\" or \"heterogeneity patterns\". More precisely, the conditional distribution of $Y$ given its causal predictors remains invariant for all observations. Recent work e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.08058","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":""},"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":"2026-05-18T00:14:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c9CkrJKWgLqeSNCE5+IbWxl9hGR4BRwFZ4Eb6OGZL24fA95yEZTz2uZ2NfTyc0Kk7m7PId8myuA488Hpy045DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T23:05:11.120394Z"},"content_sha256":"703f28a0b6dd5acb00e32b11c5f401f65040a633ec00ec734b12b2147930eebd","schema_version":"1.0","event_id":"sha256:703f28a0b6dd5acb00e32b11c5f401f65040a633ec00ec734b12b2147930eebd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4D6HFNJG3GTVB5FZSABRWKIILL/bundle.json","state_url":"https://pith.science/pith/4D6HFNJG3GTVB5FZSABRWKIILL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4D6HFNJG3GTVB5FZSABRWKIILL/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":"2026-05-28T23:05:11Z","links":{"resolver":"https://pith.science/pith/4D6HFNJG3GTVB5FZSABRWKIILL","bundle":"https://pith.science/pith/4D6HFNJG3GTVB5FZSABRWKIILL/bundle.json","state":"https://pith.science/pith/4D6HFNJG3GTVB5FZSABRWKIILL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4D6HFNJG3GTVB5FZSABRWKIILL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:4D6HFNJG3GTVB5FZSABRWKIILL","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":"73a948127b018be345b33f3fbb4db53973f994db06495477c3d31ab1942160b2","cross_cats_sorted":["stat.AP","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-06-25T08:25:25Z","title_canon_sha256":"e1ff778fa91943973abd58db906fd6a2e58e4c7a06ba219ef7f64a2a23a390e6"},"schema_version":"1.0","source":{"id":"1706.08058","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.08058","created_at":"2026-05-18T00:14:56Z"},{"alias_kind":"arxiv_version","alias_value":"1706.08058v2","created_at":"2026-05-18T00:14:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.08058","created_at":"2026-05-18T00:14:56Z"},{"alias_kind":"pith_short_12","alias_value":"4D6HFNJG3GTV","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"4D6HFNJG3GTVB5FZ","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"4D6HFNJG","created_at":"2026-05-18T12:30:58Z"}],"graph_snapshots":[{"event_id":"sha256:703f28a0b6dd5acb00e32b11c5f401f65040a633ec00ec734b12b2147930eebd","target":"graph","created_at":"2026-05-18T00:14:56Z","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"},"paper":{"abstract_excerpt":"We investigate the problem of inferring the causal predictors of a response $Y$ from a set of $d$ explanatory variables $(X^1,\\dots,X^d)$. Classical ordinary least squares regression includes all predictors that reduce the variance of $Y$. Using only the causal predictors instead leads to models that have the advantage of remaining invariant under interventions, loosely speaking they lead to invariance across different \"environments\" or \"heterogeneity patterns\". More precisely, the conditional distribution of $Y$ given its causal predictors remains invariant for all observations. Recent work e","authors_text":"Jonas Peters, Niklas Pfister, Peter B\\\"uhlmann","cross_cats":["stat.AP","stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-06-25T08:25:25Z","title":"Invariant Causal Prediction for Sequential Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.08058","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":"sha256:0b8dd016f6653a2d6e9f5c6a1ac352a5790f9c1e9d564dc1fee6349a112736eb","target":"record","created_at":"2026-05-18T00:14:56Z","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":"73a948127b018be345b33f3fbb4db53973f994db06495477c3d31ab1942160b2","cross_cats_sorted":["stat.AP","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-06-25T08:25:25Z","title_canon_sha256":"e1ff778fa91943973abd58db906fd6a2e58e4c7a06ba219ef7f64a2a23a390e6"},"schema_version":"1.0","source":{"id":"1706.08058","kind":"arxiv","version":2}},"canonical_sha256":"e0fc72b526d9a750f4b990031b29085ac5bf1e9a43d50a95aefb3e439048192e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e0fc72b526d9a750f4b990031b29085ac5bf1e9a43d50a95aefb3e439048192e","first_computed_at":"2026-05-18T00:14:56.906133Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:56.906133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p68iT20g8xJ+y9QJWKijVP8ZjB1yvOKrhwzNnQFYLxXATabCX2hjCfbknrWcMDm90uZigMPgHhAGkTFuybnnCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:56.906752Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.08058","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b8dd016f6653a2d6e9f5c6a1ac352a5790f9c1e9d564dc1fee6349a112736eb","sha256:703f28a0b6dd5acb00e32b11c5f401f65040a633ec00ec734b12b2147930eebd"],"state_sha256":"3bd969a4a2efad5b1096ab8902afd3ba030cba7bb03978b34ca519fe622c00ab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l14AMA9vfoxFspOoLuWDRspqKmtdaW27+WQBuMNApkTkY1r+t/73fVc0hXDe3tDrqBvfvt6jQSeddhw4NtqhAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T23:05:11.122733Z","bundle_sha256":"6c32f567192e30bc040e9d3eebd83ebc07a473adbf2e8c7433c5bf39a5135465"}}