{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:RGFC4V54AVOPGQKMUDSCLNEGWP","short_pith_number":"pith:RGFC4V54","canonical_record":{"source":{"id":"1606.03976","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-13T14:40:57Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"732b823508145610b8051d8a47ca750d00dafdc7f79802a437ef98258cfdebed","abstract_canon_sha256":"85b0104458b704f9669dfdcac223122698797b7ccf57ba1234f16e962b856647"},"schema_version":"1.0"},"canonical_sha256":"898a2e57bc055cf3414ca0e425b486b3f0c9f060b8639ff6f5a93368cf918981","source":{"kind":"arxiv","id":"1606.03976","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.03976","created_at":"2026-05-18T00:44:24Z"},{"alias_kind":"arxiv_version","alias_value":"1606.03976v5","created_at":"2026-05-18T00:44:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.03976","created_at":"2026-05-18T00:44:24Z"},{"alias_kind":"pith_short_12","alias_value":"RGFC4V54AVOP","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RGFC4V54AVOPGQKM","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RGFC4V54","created_at":"2026-05-18T12:30:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:RGFC4V54AVOPGQKMUDSCLNEGWP","target":"record","payload":{"canonical_record":{"source":{"id":"1606.03976","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-13T14:40:57Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"732b823508145610b8051d8a47ca750d00dafdc7f79802a437ef98258cfdebed","abstract_canon_sha256":"85b0104458b704f9669dfdcac223122698797b7ccf57ba1234f16e962b856647"},"schema_version":"1.0"},"canonical_sha256":"898a2e57bc055cf3414ca0e425b486b3f0c9f060b8639ff6f5a93368cf918981","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:24.652713Z","signature_b64":"1YJXDjlOCeXZ2MTXY3Tm8+lMh1WEou9vTc8PbVuc9Y7wgt/u9XlNgWbk92m/uDemukf5qOPSpwOmjsuBa+UgAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"898a2e57bc055cf3414ca0e425b486b3f0c9f060b8639ff6f5a93368cf918981","last_reissued_at":"2026-05-18T00:44:24.652241Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:24.652241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.03976","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:44:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vY0n14hLN1DtS6+N0A8me3mC8ZbNnZItx1U1mcS+fG31l7dc28Zwc3QrPSzkXd1MQl58PaVn0MhkEk9N3wyGCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T12:11:56.746635Z"},"content_sha256":"97d7690beb8ff6e75c0e1037300c1c1a15459559b05e6c2862d0d25a3db3b0e5","schema_version":"1.0","event_id":"sha256:97d7690beb8ff6e75c0e1037300c1c1a15459559b05e6c2862d0d25a3db3b0e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:RGFC4V54AVOPGQKMUDSCLNEGWP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Estimating individual treatment effect: generalization bounds and algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"David Sontag, Fredrik D. Johansson, Uri Shalit","submitted_at":"2016-06-13T14:40:57Z","abstract_excerpt":"There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a \"balanced\" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalizati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.03976","kind":"arxiv","version":5},"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":"2026-05-18T00:44:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WpI4BESyzRQoF+EL8X16K8tmENAkkbrcsU2HZSboOkGj+yl7v+Dd2AvhcWpoUN7dSzomX8JJkg8b+blXqhsMBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T12:11:56.747350Z"},"content_sha256":"dc60337df234ebb93bef203adbf76b1e4962f8cbcbc0fb3ccea8b8c92a4ba800","schema_version":"1.0","event_id":"sha256:dc60337df234ebb93bef203adbf76b1e4962f8cbcbc0fb3ccea8b8c92a4ba800"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RGFC4V54AVOPGQKMUDSCLNEGWP/bundle.json","state_url":"https://pith.science/pith/RGFC4V54AVOPGQKMUDSCLNEGWP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RGFC4V54AVOPGQKMUDSCLNEGWP/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":"2026-05-27T12:11:56Z","links":{"resolver":"https://pith.science/pith/RGFC4V54AVOPGQKMUDSCLNEGWP","bundle":"https://pith.science/pith/RGFC4V54AVOPGQKMUDSCLNEGWP/bundle.json","state":"https://pith.science/pith/RGFC4V54AVOPGQKMUDSCLNEGWP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RGFC4V54AVOPGQKMUDSCLNEGWP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:RGFC4V54AVOPGQKMUDSCLNEGWP","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":"85b0104458b704f9669dfdcac223122698797b7ccf57ba1234f16e962b856647","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-13T14:40:57Z","title_canon_sha256":"732b823508145610b8051d8a47ca750d00dafdc7f79802a437ef98258cfdebed"},"schema_version":"1.0","source":{"id":"1606.03976","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.03976","created_at":"2026-05-18T00:44:24Z"},{"alias_kind":"arxiv_version","alias_value":"1606.03976v5","created_at":"2026-05-18T00:44:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.03976","created_at":"2026-05-18T00:44:24Z"},{"alias_kind":"pith_short_12","alias_value":"RGFC4V54AVOP","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RGFC4V54AVOPGQKM","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RGFC4V54","created_at":"2026-05-18T12:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:dc60337df234ebb93bef203adbf76b1e4962f8cbcbc0fb3ccea8b8c92a4ba800","target":"graph","created_at":"2026-05-18T00:44:24Z","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":"There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a \"balanced\" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalizati","authors_text":"David Sontag, Fredrik D. Johansson, Uri Shalit","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-13T14:40:57Z","title":"Estimating individual treatment effect: generalization bounds and algorithms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.03976","kind":"arxiv","version":5},"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":"sha256:97d7690beb8ff6e75c0e1037300c1c1a15459559b05e6c2862d0d25a3db3b0e5","target":"record","created_at":"2026-05-18T00:44:24Z","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":"85b0104458b704f9669dfdcac223122698797b7ccf57ba1234f16e962b856647","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-13T14:40:57Z","title_canon_sha256":"732b823508145610b8051d8a47ca750d00dafdc7f79802a437ef98258cfdebed"},"schema_version":"1.0","source":{"id":"1606.03976","kind":"arxiv","version":5}},"canonical_sha256":"898a2e57bc055cf3414ca0e425b486b3f0c9f060b8639ff6f5a93368cf918981","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"898a2e57bc055cf3414ca0e425b486b3f0c9f060b8639ff6f5a93368cf918981","first_computed_at":"2026-05-18T00:44:24.652241Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:44:24.652241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1YJXDjlOCeXZ2MTXY3Tm8+lMh1WEou9vTc8PbVuc9Y7wgt/u9XlNgWbk92m/uDemukf5qOPSpwOmjsuBa+UgAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:44:24.652713Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.03976","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:97d7690beb8ff6e75c0e1037300c1c1a15459559b05e6c2862d0d25a3db3b0e5","sha256:dc60337df234ebb93bef203adbf76b1e4962f8cbcbc0fb3ccea8b8c92a4ba800"],"state_sha256":"314b818c570fddc9d5842f14ce786d168e9d8fc055a97713444b2849cf79386b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j2zHgryxuW/6XxdxaEigYRu0tG13iQ3A645TfTQAerWKHT9fHyewtEldVbtDXZZ0R0XUa09XegDnkpB3+pbCDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T12:11:56.750795Z","bundle_sha256":"2d0d015b0ade3a6ee46ab01c9a36ce991134f35044aaa09080e9a9083ca95fa7"}}