{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:4MZXYNM5HH4S37M7TFDMSJLNOW","short_pith_number":"pith:4MZXYNM5","canonical_record":{"source":{"id":"1511.00041","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2015-10-30T22:24:13Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT","stat.ML"],"title_canon_sha256":"2be9b451349d9be19101867c508be9033eb553055e4ceb0ff8c075f864cc518e","abstract_canon_sha256":"34499dc2315940405474ba8d15635b656a3ed0436138824f4f149c6e0213c5ec"},"schema_version":"1.0"},"canonical_sha256":"e3337c359d39f92dfd9f9946c9256d75b5496a85052e06b208c5ca5f584c81a6","source":{"kind":"arxiv","id":"1511.00041","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.00041","created_at":"2026-05-18T01:28:13Z"},{"alias_kind":"arxiv_version","alias_value":"1511.00041v1","created_at":"2026-05-18T01:28:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.00041","created_at":"2026-05-18T01:28:13Z"},{"alias_kind":"pith_short_12","alias_value":"4MZXYNM5HH4S","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"4MZXYNM5HH4S37M7","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"4MZXYNM5","created_at":"2026-05-18T12:29:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:4MZXYNM5HH4S37M7TFDMSJLNOW","target":"record","payload":{"canonical_record":{"source":{"id":"1511.00041","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2015-10-30T22:24:13Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT","stat.ML"],"title_canon_sha256":"2be9b451349d9be19101867c508be9033eb553055e4ceb0ff8c075f864cc518e","abstract_canon_sha256":"34499dc2315940405474ba8d15635b656a3ed0436138824f4f149c6e0213c5ec"},"schema_version":"1.0"},"canonical_sha256":"e3337c359d39f92dfd9f9946c9256d75b5496a85052e06b208c5ca5f584c81a6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:28:13.521334Z","signature_b64":"rrgtpbux9YCsA9EOzITWy0NYFm0edEXojuiIKXMo9SNNvhyXNXvqMPYIPeQ/hNjT3DgO/2DgczJ84l2f4jHBDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3337c359d39f92dfd9f9946c9256d75b5496a85052e06b208c5ca5f584c81a6","last_reissued_at":"2026-05-18T01:28:13.520583Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:28:13.520583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.00041","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":"2026-05-18T01:28:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G/EDORXuxLHx92THkGzO05djh5ZJpfuxszXnMY+1aV4ku7zocGal7or+C5hBRtOIlYjrRNBXYIfFoRSoKeAcCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T17:49:14.638284Z"},"content_sha256":"634d540f869c94656fa106e9dd811ab2f999ba0d76f0c6c2d63d8f7eb84821f3","schema_version":"1.0","event_id":"sha256:634d540f869c94656fa106e9dd811ab2f999ba0d76f0c6c2d63d8f7eb84821f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:4MZXYNM5HH4S37M7TFDMSJLNOW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Causal Graphs with Small Interventions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT","stat.ML"],"primary_cat":"cs.AI","authors_text":"Alexandros G. Dimakis, Karthikeyan Shanmugam, Murat Kocaoglu, Sriram Vishwanath","submitted_at":"2015-10-30T22:24:13Z","abstract_excerpt":"We consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (SEM-IE). The objective is to minimize the number of experiments to discover the causal directions of all the edges in a causal graph. Previous work has focused on the use of separating systems for complete graphs for this task. We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in the worst case. In addition, we present a novel separating system constru"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.00041","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":"2026-05-18T01:28:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cU1/Ut0mroc9AwQHMfRVe+8SeVVpJY0asLymW9Ylmq988xjhGA887xcKKz5dc6ZU9OUvpwlkxovcm3ieTpGRCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T17:49:14.638982Z"},"content_sha256":"26c84a44acf84e1c80696123d6c0d8b6554d388ec9e7095bf6013f19ccedf9ae","schema_version":"1.0","event_id":"sha256:26c84a44acf84e1c80696123d6c0d8b6554d388ec9e7095bf6013f19ccedf9ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4MZXYNM5HH4S37M7TFDMSJLNOW/bundle.json","state_url":"https://pith.science/pith/4MZXYNM5HH4S37M7TFDMSJLNOW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4MZXYNM5HH4S37M7TFDMSJLNOW/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":"2026-05-19T17:49:14Z","links":{"resolver":"https://pith.science/pith/4MZXYNM5HH4S37M7TFDMSJLNOW","bundle":"https://pith.science/pith/4MZXYNM5HH4S37M7TFDMSJLNOW/bundle.json","state":"https://pith.science/pith/4MZXYNM5HH4S37M7TFDMSJLNOW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4MZXYNM5HH4S37M7TFDMSJLNOW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:4MZXYNM5HH4S37M7TFDMSJLNOW","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":"34499dc2315940405474ba8d15635b656a3ed0436138824f4f149c6e0213c5ec","cross_cats_sorted":["cs.IT","cs.LG","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2015-10-30T22:24:13Z","title_canon_sha256":"2be9b451349d9be19101867c508be9033eb553055e4ceb0ff8c075f864cc518e"},"schema_version":"1.0","source":{"id":"1511.00041","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.00041","created_at":"2026-05-18T01:28:13Z"},{"alias_kind":"arxiv_version","alias_value":"1511.00041v1","created_at":"2026-05-18T01:28:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.00041","created_at":"2026-05-18T01:28:13Z"},{"alias_kind":"pith_short_12","alias_value":"4MZXYNM5HH4S","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"4MZXYNM5HH4S37M7","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"4MZXYNM5","created_at":"2026-05-18T12:29:05Z"}],"graph_snapshots":[{"event_id":"sha256:26c84a44acf84e1c80696123d6c0d8b6554d388ec9e7095bf6013f19ccedf9ae","target":"graph","created_at":"2026-05-18T01:28:13Z","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 consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (SEM-IE). The objective is to minimize the number of experiments to discover the causal directions of all the edges in a causal graph. Previous work has focused on the use of separating systems for complete graphs for this task. We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in the worst case. In addition, we present a novel separating system constru","authors_text":"Alexandros G. Dimakis, Karthikeyan Shanmugam, Murat Kocaoglu, Sriram Vishwanath","cross_cats":["cs.IT","cs.LG","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2015-10-30T22:24:13Z","title":"Learning Causal Graphs with Small Interventions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.00041","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":"sha256:634d540f869c94656fa106e9dd811ab2f999ba0d76f0c6c2d63d8f7eb84821f3","target":"record","created_at":"2026-05-18T01:28:13Z","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":"34499dc2315940405474ba8d15635b656a3ed0436138824f4f149c6e0213c5ec","cross_cats_sorted":["cs.IT","cs.LG","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2015-10-30T22:24:13Z","title_canon_sha256":"2be9b451349d9be19101867c508be9033eb553055e4ceb0ff8c075f864cc518e"},"schema_version":"1.0","source":{"id":"1511.00041","kind":"arxiv","version":1}},"canonical_sha256":"e3337c359d39f92dfd9f9946c9256d75b5496a85052e06b208c5ca5f584c81a6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e3337c359d39f92dfd9f9946c9256d75b5496a85052e06b208c5ca5f584c81a6","first_computed_at":"2026-05-18T01:28:13.520583Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:28:13.520583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rrgtpbux9YCsA9EOzITWy0NYFm0edEXojuiIKXMo9SNNvhyXNXvqMPYIPeQ/hNjT3DgO/2DgczJ84l2f4jHBDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:28:13.521334Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.00041","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:634d540f869c94656fa106e9dd811ab2f999ba0d76f0c6c2d63d8f7eb84821f3","sha256:26c84a44acf84e1c80696123d6c0d8b6554d388ec9e7095bf6013f19ccedf9ae"],"state_sha256":"76a54ecc49ee36caa7dab20e6508299e8d2009508cd2a9e3521a8d6d2d679b0e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WRnqRyG60EDAwPDiWSxW5LLsbq58UCCNzaKmomQF7xfONrGLl3zX8NtllHLDQ/De06tC/mfWkBQYM41jXsdKAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T17:49:14.642813Z","bundle_sha256":"ac7be1aabf34671a407bbc012540d5feeaceb53b6f3786bb64616189aceff981"}}