{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:HVHA7ZQRKGJGTJJGU4GUJIGBBA","short_pith_number":"pith:HVHA7ZQR","canonical_record":{"source":{"id":"1202.3763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-02-14T16:41:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e0c84be5dcba1eeaf49deecfcb6a078362c8bd1701c7779967cf77b071fa3732","abstract_canon_sha256":"b212c68a7d07a87b0644f98a82b142bc3568b47544a688526f23b24a2d3ffa99"},"schema_version":"1.0"},"canonical_sha256":"3d4e0fe611519269a526a70d44a0c1082190fe9cf8e057d0159f73855a8717a2","source":{"kind":"arxiv","id":"1202.3763","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1202.3763","created_at":"2026-05-18T04:02:02Z"},{"alias_kind":"arxiv_version","alias_value":"1202.3763v1","created_at":"2026-05-18T04:02:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1202.3763","created_at":"2026-05-18T04:02:02Z"},{"alias_kind":"pith_short_12","alias_value":"HVHA7ZQRKGJG","created_at":"2026-05-18T12:27:09Z"},{"alias_kind":"pith_short_16","alias_value":"HVHA7ZQRKGJGTJJG","created_at":"2026-05-18T12:27:09Z"},{"alias_kind":"pith_short_8","alias_value":"HVHA7ZQR","created_at":"2026-05-18T12:27:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:HVHA7ZQRKGJGTJJGU4GUJIGBBA","target":"record","payload":{"canonical_record":{"source":{"id":"1202.3763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-02-14T16:41:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e0c84be5dcba1eeaf49deecfcb6a078362c8bd1701c7779967cf77b071fa3732","abstract_canon_sha256":"b212c68a7d07a87b0644f98a82b142bc3568b47544a688526f23b24a2d3ffa99"},"schema_version":"1.0"},"canonical_sha256":"3d4e0fe611519269a526a70d44a0c1082190fe9cf8e057d0159f73855a8717a2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:02:02.975976Z","signature_b64":"edg1wASAF4toq8X7RGxQwk23T7JimO8a2ZEC3HU/jrh2tosBtVr6aRPdUcuesLSifzytBBRpH2ocStfXNNFDDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d4e0fe611519269a526a70d44a0c1082190fe9cf8e057d0159f73855a8717a2","last_reissued_at":"2026-05-18T04:02:02.975428Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:02:02.975428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1202.3763","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":"2026-05-18T04:02:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W3tkCt5FTKA68uFphQnUQdD4gPGhMZ8oYBO1AAsM8KI9v7rAciR0lAHHf7Fg6/yTDU1fi4ed8U5p96VCqyVXAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T21:37:07.800673Z"},"content_sha256":"b63e7cba727df4301615c37494ca0f17d984891d07872fbe1b43b56d6b13e4c9","schema_version":"1.0","event_id":"sha256:b63e7cba727df4301615c37494ca0f17d984891d07872fbe1b43b56d6b13e4c9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:HVHA7ZQRKGJGTJJGU4GUJIGBBA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ilya Shpitser, James M. Robins, Thomas S. Richardson","submitted_at":"2012-02-14T16:41:17Z","abstract_excerpt":"Probabilistic inference in graphical models is the task of computing marginal and conditional densities of interest from a factorized representation of a joint probability distribution. Inference algorithms such as variable elimination and belief propagation take advantage of constraints embedded in this factorization to compute such densities efficiently. In this paper, we propose an algorithm which computes interventional distributions in latent variable causal models represented by acyclic directed mixed graphs(ADMGs). To compute these distributions efficiently, we take advantage of a recur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1202.3763","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":""},"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":"2026-05-18T04:02:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3yPSaF/UV9zOkBRdQvZ+zIasxKfC0HIp6SzmPniIrwPV8U9qhTjrJYopPLAN9/YwqwDvIxbkYhaF0siaCI9FAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T21:37:07.801245Z"},"content_sha256":"9f3f60ddf701c0e845878f180c50f8765e9519bf62757795e1a2c3116e8f7018","schema_version":"1.0","event_id":"sha256:9f3f60ddf701c0e845878f180c50f8765e9519bf62757795e1a2c3116e8f7018"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HVHA7ZQRKGJGTJJGU4GUJIGBBA/bundle.json","state_url":"https://pith.science/pith/HVHA7ZQRKGJGTJJGU4GUJIGBBA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HVHA7ZQRKGJGTJJGU4GUJIGBBA/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":"2026-05-30T21:37:07Z","links":{"resolver":"https://pith.science/pith/HVHA7ZQRKGJGTJJGU4GUJIGBBA","bundle":"https://pith.science/pith/HVHA7ZQRKGJGTJJGU4GUJIGBBA/bundle.json","state":"https://pith.science/pith/HVHA7ZQRKGJGTJJGU4GUJIGBBA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HVHA7ZQRKGJGTJJGU4GUJIGBBA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:HVHA7ZQRKGJGTJJGU4GUJIGBBA","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":"b212c68a7d07a87b0644f98a82b142bc3568b47544a688526f23b24a2d3ffa99","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-02-14T16:41:17Z","title_canon_sha256":"e0c84be5dcba1eeaf49deecfcb6a078362c8bd1701c7779967cf77b071fa3732"},"schema_version":"1.0","source":{"id":"1202.3763","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1202.3763","created_at":"2026-05-18T04:02:02Z"},{"alias_kind":"arxiv_version","alias_value":"1202.3763v1","created_at":"2026-05-18T04:02:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1202.3763","created_at":"2026-05-18T04:02:02Z"},{"alias_kind":"pith_short_12","alias_value":"HVHA7ZQRKGJG","created_at":"2026-05-18T12:27:09Z"},{"alias_kind":"pith_short_16","alias_value":"HVHA7ZQRKGJGTJJG","created_at":"2026-05-18T12:27:09Z"},{"alias_kind":"pith_short_8","alias_value":"HVHA7ZQR","created_at":"2026-05-18T12:27:09Z"}],"graph_snapshots":[{"event_id":"sha256:9f3f60ddf701c0e845878f180c50f8765e9519bf62757795e1a2c3116e8f7018","target":"graph","created_at":"2026-05-18T04:02:02Z","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":"Probabilistic inference in graphical models is the task of computing marginal and conditional densities of interest from a factorized representation of a joint probability distribution. Inference algorithms such as variable elimination and belief propagation take advantage of constraints embedded in this factorization to compute such densities efficiently. In this paper, we propose an algorithm which computes interventional distributions in latent variable causal models represented by acyclic directed mixed graphs(ADMGs). To compute these distributions efficiently, we take advantage of a recur","authors_text":"Ilya Shpitser, James M. Robins, Thomas S. Richardson","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-02-14T16:41:17Z","title":"An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1202.3763","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:b63e7cba727df4301615c37494ca0f17d984891d07872fbe1b43b56d6b13e4c9","target":"record","created_at":"2026-05-18T04:02:02Z","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":"b212c68a7d07a87b0644f98a82b142bc3568b47544a688526f23b24a2d3ffa99","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-02-14T16:41:17Z","title_canon_sha256":"e0c84be5dcba1eeaf49deecfcb6a078362c8bd1701c7779967cf77b071fa3732"},"schema_version":"1.0","source":{"id":"1202.3763","kind":"arxiv","version":1}},"canonical_sha256":"3d4e0fe611519269a526a70d44a0c1082190fe9cf8e057d0159f73855a8717a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d4e0fe611519269a526a70d44a0c1082190fe9cf8e057d0159f73855a8717a2","first_computed_at":"2026-05-18T04:02:02.975428Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:02:02.975428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"edg1wASAF4toq8X7RGxQwk23T7JimO8a2ZEC3HU/jrh2tosBtVr6aRPdUcuesLSifzytBBRpH2ocStfXNNFDDw==","signature_status":"signed_v1","signed_at":"2026-05-18T04:02:02.975976Z","signed_message":"canonical_sha256_bytes"},"source_id":"1202.3763","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b63e7cba727df4301615c37494ca0f17d984891d07872fbe1b43b56d6b13e4c9","sha256:9f3f60ddf701c0e845878f180c50f8765e9519bf62757795e1a2c3116e8f7018"],"state_sha256":"c1c0359561db65c56a6667f8ee90a6bdbc16e16e55399ca1f59cd35af761dc79"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g5XZlQOKCOyEiXzj/1CAYoV5gibKQPR5ndcI3InApQ45aGvyepPscUcG3qyNEG/DU/iVbfKccFIejG/YPe3ZDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T21:37:07.804568Z","bundle_sha256":"6661c5a877250024055362acc70d380cf57e3b4d9699ea48849b3511690169fc"}}