{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:JAMSQATBNWOB5YD27QMVWFCAR3","short_pith_number":"pith:JAMSQATB","canonical_record":{"source":{"id":"1705.08821","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-24T15:33:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a280d6dc1d1384b11858a78a4d18c9ead70ef35166e4620d07ee49a62825262b","abstract_canon_sha256":"579261ac5d1e44ca9eb9d53796b7c473ab4703256b802c6ccebd34b96c83b150"},"schema_version":"1.0"},"canonical_sha256":"48192802616d9c1ee07afc195b14408ef268037ddd1f53487d9efe8ce49f5179","source":{"kind":"arxiv","id":"1705.08821","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08821","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08821v2","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08821","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"pith_short_12","alias_value":"JAMSQATBNWOB","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"JAMSQATBNWOB5YD2","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"JAMSQATB","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:JAMSQATBNWOB5YD27QMVWFCAR3","target":"record","payload":{"canonical_record":{"source":{"id":"1705.08821","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-24T15:33:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a280d6dc1d1384b11858a78a4d18c9ead70ef35166e4620d07ee49a62825262b","abstract_canon_sha256":"579261ac5d1e44ca9eb9d53796b7c473ab4703256b802c6ccebd34b96c83b150"},"schema_version":"1.0"},"canonical_sha256":"48192802616d9c1ee07afc195b14408ef268037ddd1f53487d9efe8ce49f5179","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:19.399124Z","signature_b64":"XR6F+81/495RMzLSqQYd38j1XTqtbtz46G2EnNG7WZxoMr8VszoNuh5o1fXYGBDm2+JLnsE+k+7l3kMaFQmIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48192802616d9c1ee07afc195b14408ef268037ddd1f53487d9efe8ce49f5179","last_reissued_at":"2026-05-18T00:31:19.398392Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:19.398392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.08821","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":"2026-05-18T00:31:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wPfKA7lIw61vqQvk2UL/2bEzKwwN/MthWmwrbFGg2aUc0IgujPitVilR0FEpNg5GgKuvCr5HlX9Szg4RmDDtCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T20:16:09.059655Z"},"content_sha256":"fc988aeb03d0a9502e45e1ccd1e3cf806f4f09098c8768a00157fb6231d080f5","schema_version":"1.0","event_id":"sha256:fc988aeb03d0a9502e45e1ccd1e3cf806f4f09098c8768a00157fb6231d080f5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:JAMSQATBNWOB5YD27QMVWFCAR3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Causal Effect Inference with Deep Latent-Variable Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Christos Louizos, David Sontag, Joris Mooij, Max Welling, Richard Zemel, Uri Shalit","submitted_at":"2017-05-24T15:33:48Z","abstract_excerpt":"Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We buil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08821","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":"2026-05-18T00:31:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RXDfrS+h3WxzYKwkdtxtxNd268WOdW0u68z9LV7aOSBXjqhwOX6o16vpg+2EXgOdUPNXpH3fmQBbc2tP4krPAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T20:16:09.060013Z"},"content_sha256":"4430e0feb66c8ce83aa456ebbcebb8a693088188ec17065e86b7d1ce85dd2698","schema_version":"1.0","event_id":"sha256:4430e0feb66c8ce83aa456ebbcebb8a693088188ec17065e86b7d1ce85dd2698"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JAMSQATBNWOB5YD27QMVWFCAR3/bundle.json","state_url":"https://pith.science/pith/JAMSQATBNWOB5YD27QMVWFCAR3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JAMSQATBNWOB5YD27QMVWFCAR3/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":"2026-05-27T20:16:09Z","links":{"resolver":"https://pith.science/pith/JAMSQATBNWOB5YD27QMVWFCAR3","bundle":"https://pith.science/pith/JAMSQATBNWOB5YD27QMVWFCAR3/bundle.json","state":"https://pith.science/pith/JAMSQATBNWOB5YD27QMVWFCAR3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JAMSQATBNWOB5YD27QMVWFCAR3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:JAMSQATBNWOB5YD27QMVWFCAR3","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":"579261ac5d1e44ca9eb9d53796b7c473ab4703256b802c6ccebd34b96c83b150","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-24T15:33:48Z","title_canon_sha256":"a280d6dc1d1384b11858a78a4d18c9ead70ef35166e4620d07ee49a62825262b"},"schema_version":"1.0","source":{"id":"1705.08821","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08821","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08821v2","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08821","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"pith_short_12","alias_value":"JAMSQATBNWOB","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"JAMSQATBNWOB5YD2","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"JAMSQATB","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:4430e0feb66c8ce83aa456ebbcebb8a693088188ec17065e86b7d1ce85dd2698","target":"graph","created_at":"2026-05-18T00:31:19Z","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":"Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We buil","authors_text":"Christos Louizos, David Sontag, Joris Mooij, Max Welling, Richard Zemel, Uri Shalit","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-24T15:33:48Z","title":"Causal Effect Inference with Deep Latent-Variable Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08821","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:fc988aeb03d0a9502e45e1ccd1e3cf806f4f09098c8768a00157fb6231d080f5","target":"record","created_at":"2026-05-18T00:31:19Z","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":"579261ac5d1e44ca9eb9d53796b7c473ab4703256b802c6ccebd34b96c83b150","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-24T15:33:48Z","title_canon_sha256":"a280d6dc1d1384b11858a78a4d18c9ead70ef35166e4620d07ee49a62825262b"},"schema_version":"1.0","source":{"id":"1705.08821","kind":"arxiv","version":2}},"canonical_sha256":"48192802616d9c1ee07afc195b14408ef268037ddd1f53487d9efe8ce49f5179","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"48192802616d9c1ee07afc195b14408ef268037ddd1f53487d9efe8ce49f5179","first_computed_at":"2026-05-18T00:31:19.398392Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:19.398392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XR6F+81/495RMzLSqQYd38j1XTqtbtz46G2EnNG7WZxoMr8VszoNuh5o1fXYGBDm2+JLnsE+k+7l3kMaFQmIDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:19.399124Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.08821","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc988aeb03d0a9502e45e1ccd1e3cf806f4f09098c8768a00157fb6231d080f5","sha256:4430e0feb66c8ce83aa456ebbcebb8a693088188ec17065e86b7d1ce85dd2698"],"state_sha256":"74e35e6638a7a8caf8f86b12bff75f6b2da2afd98611d0a6cd79e2e8c15a6c2f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bTCLyLrazoIB3tqY8eTN//GirggqTcdRqqDvCR8GPY8O0XMBkUtqJz/7tRGa1n32t25DuuAKZikalwjBInjfAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T20:16:09.062192Z","bundle_sha256":"7198dcc022e99c50fab9d4b3106f79e0f00e0a9ccd101592970c2fc5db23c866"}}