{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IVHMQCNX6QFQKJ7Y7QMZGFTNHF","short_pith_number":"pith:IVHMQCNX","canonical_record":{"source":{"id":"1805.03743","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-05-09T22:10:19Z","cross_cats_sorted":[],"title_canon_sha256":"bf88bfef0129c27ffecffd7b5a34d651aadeaa8d143358722c4fe53a802a85b9","abstract_canon_sha256":"89ccf16846a312f1a824a8f06bbe31b9ddb3b168a6818ab86f9db1ab08fd78e9"},"schema_version":"1.0"},"canonical_sha256":"454ec809b7f40b0527f8fc1993166d394916b3b05880e0c9041ea96e2e48d042","source":{"kind":"arxiv","id":"1805.03743","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.03743","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"arxiv_version","alias_value":"1805.03743v3","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.03743","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"pith_short_12","alias_value":"IVHMQCNX6QFQ","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IVHMQCNX6QFQKJ7Y","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IVHMQCNX","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IVHMQCNX6QFQKJ7Y7QMZGFTNHF","target":"record","payload":{"canonical_record":{"source":{"id":"1805.03743","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-05-09T22:10:19Z","cross_cats_sorted":[],"title_canon_sha256":"bf88bfef0129c27ffecffd7b5a34d651aadeaa8d143358722c4fe53a802a85b9","abstract_canon_sha256":"89ccf16846a312f1a824a8f06bbe31b9ddb3b168a6818ab86f9db1ab08fd78e9"},"schema_version":"1.0"},"canonical_sha256":"454ec809b7f40b0527f8fc1993166d394916b3b05880e0c9041ea96e2e48d042","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:47.978310Z","signature_b64":"EooHks8BTYmBqTHoyO1daAuPRSNymxVIoSeQHuOCtt9lNSMJcT0IM3tBpavU2H8gPs+JVRsaqxrS4283PslXAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"454ec809b7f40b0527f8fc1993166d394916b3b05880e0c9041ea96e2e48d042","last_reissued_at":"2026-05-17T23:56:47.977918Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:47.977918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.03743","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:56:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DKHhngkLMrbFOBfgtF8mdWuVfzuUsj5Mt90HHEm+yqUFU0xvv0AD2F4u4uU/t+QtPWw/xn7qAsbYSteMu62vAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T02:16:39.846641Z"},"content_sha256":"1182dc354750985ed5d4d1da1558653bd0e1ae8cf9fb87e79127971a4ba26863","schema_version":"1.0","event_id":"sha256:1182dc354750985ed5d4d1da1558653bd0e1ae8cf9fb87e79127971a4ba26863"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IVHMQCNX6QFQKJ7Y7QMZGFTNHF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference using Five Empirical Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Dylan Small, Luke Keele","submitted_at":"2018-05-09T22:10:19Z","abstract_excerpt":"When investigators seek to estimate causal effects, they often assume that selection into treatment is based only on observed covariates. Under this identification strategy, analysts must adjust for observed confounders. While basic regression models have long been the dominant method of statistical adjustment, more robust methods based on matching or weighting have become more common. Of late, even more flexible methods based on machine learning methods have been developed for statistical adjustment. These machine learning methods are designed to be black box methods with little input from th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.03743","kind":"arxiv","version":3},"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":"2026-05-17T23:56:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nfb3TF0jiuodIxriIs0akloBVLPO4aeghqo4z/aVTBnChhKf4xZZupADx6Fb/IBJ5L6flVTR4xDE4EKbCWJ0BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T02:16:39.846989Z"},"content_sha256":"fdc75b4d309e6393dbb48f3c181e2a6a0b1360ef8db1b307e723a6123cc45926","schema_version":"1.0","event_id":"sha256:fdc75b4d309e6393dbb48f3c181e2a6a0b1360ef8db1b307e723a6123cc45926"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IVHMQCNX6QFQKJ7Y7QMZGFTNHF/bundle.json","state_url":"https://pith.science/pith/IVHMQCNX6QFQKJ7Y7QMZGFTNHF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IVHMQCNX6QFQKJ7Y7QMZGFTNHF/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":"2026-06-07T02:16:39Z","links":{"resolver":"https://pith.science/pith/IVHMQCNX6QFQKJ7Y7QMZGFTNHF","bundle":"https://pith.science/pith/IVHMQCNX6QFQKJ7Y7QMZGFTNHF/bundle.json","state":"https://pith.science/pith/IVHMQCNX6QFQKJ7Y7QMZGFTNHF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IVHMQCNX6QFQKJ7Y7QMZGFTNHF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IVHMQCNX6QFQKJ7Y7QMZGFTNHF","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":"89ccf16846a312f1a824a8f06bbe31b9ddb3b168a6818ab86f9db1ab08fd78e9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-05-09T22:10:19Z","title_canon_sha256":"bf88bfef0129c27ffecffd7b5a34d651aadeaa8d143358722c4fe53a802a85b9"},"schema_version":"1.0","source":{"id":"1805.03743","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.03743","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"arxiv_version","alias_value":"1805.03743v3","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.03743","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"pith_short_12","alias_value":"IVHMQCNX6QFQ","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IVHMQCNX6QFQKJ7Y","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IVHMQCNX","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:fdc75b4d309e6393dbb48f3c181e2a6a0b1360ef8db1b307e723a6123cc45926","target":"graph","created_at":"2026-05-17T23:56:47Z","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":"When investigators seek to estimate causal effects, they often assume that selection into treatment is based only on observed covariates. Under this identification strategy, analysts must adjust for observed confounders. While basic regression models have long been the dominant method of statistical adjustment, more robust methods based on matching or weighting have become more common. Of late, even more flexible methods based on machine learning methods have been developed for statistical adjustment. These machine learning methods are designed to be black box methods with little input from th","authors_text":"Dylan Small, Luke Keele","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-05-09T22:10:19Z","title":"Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference using Five Empirical Applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.03743","kind":"arxiv","version":3},"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":"sha256:1182dc354750985ed5d4d1da1558653bd0e1ae8cf9fb87e79127971a4ba26863","target":"record","created_at":"2026-05-17T23:56:47Z","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":"89ccf16846a312f1a824a8f06bbe31b9ddb3b168a6818ab86f9db1ab08fd78e9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-05-09T22:10:19Z","title_canon_sha256":"bf88bfef0129c27ffecffd7b5a34d651aadeaa8d143358722c4fe53a802a85b9"},"schema_version":"1.0","source":{"id":"1805.03743","kind":"arxiv","version":3}},"canonical_sha256":"454ec809b7f40b0527f8fc1993166d394916b3b05880e0c9041ea96e2e48d042","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"454ec809b7f40b0527f8fc1993166d394916b3b05880e0c9041ea96e2e48d042","first_computed_at":"2026-05-17T23:56:47.977918Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:47.977918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EooHks8BTYmBqTHoyO1daAuPRSNymxVIoSeQHuOCtt9lNSMJcT0IM3tBpavU2H8gPs+JVRsaqxrS4283PslXAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:47.978310Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.03743","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1182dc354750985ed5d4d1da1558653bd0e1ae8cf9fb87e79127971a4ba26863","sha256:fdc75b4d309e6393dbb48f3c181e2a6a0b1360ef8db1b307e723a6123cc45926"],"state_sha256":"c7cdc4ae0cf399ed44bb8dfdc91d55daaa8f0544dd5fe78eb85de8a24cfd7ee4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1iIz/teeOFVIi5fIllQOJhIqSgqMHWwXmMs3jDfUB02y34IWLFdyzOykFIMNR+8nKo0vAEUGJlHracwIK8xNBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T02:16:39.849239Z","bundle_sha256":"2c640e175f5c5c968601f62b8ab8a677962740c4c73e3be59dba64bf3a1d58d6"}}