{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:FM7TNOE25JNOKUL5PRP2WDLABL","short_pith_number":"pith:FM7TNOE2","canonical_record":{"source":{"id":"1705.08020","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-05-22T21:57:28Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"c2930e5a110f46fe2aa961233536cb58db94275e7ea663b7787c5c0438e13191","abstract_canon_sha256":"89d1f63ca50e5da0fde97b479e27a4adfcc79d074a5568228601f50fbcae1ac7"},"schema_version":"1.0"},"canonical_sha256":"2b3f36b89aea5ae5517d7c5fab0d600af07a4b4397b5d04a5ff0fa605ebc5a1f","source":{"kind":"arxiv","id":"1705.08020","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08020","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08020v4","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08020","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"pith_short_12","alias_value":"FM7TNOE25JNO","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"pith_short_16","alias_value":"FM7TNOE25JNOKUL5","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"pith_short_8","alias_value":"FM7TNOE2","created_at":"2026-07-05T03:33:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:FM7TNOE25JNOKUL5PRP2WDLABL","target":"record","payload":{"canonical_record":{"source":{"id":"1705.08020","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-05-22T21:57:28Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"c2930e5a110f46fe2aa961233536cb58db94275e7ea663b7787c5c0438e13191","abstract_canon_sha256":"89d1f63ca50e5da0fde97b479e27a4adfcc79d074a5568228601f50fbcae1ac7"},"schema_version":"1.0"},"canonical_sha256":"2b3f36b89aea5ae5517d7c5fab0d600af07a4b4397b5d04a5ff0fa605ebc5a1f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:33:36.098274Z","signature_b64":"jM5IxyW15MjYC0A8UTKGDZLIoVss+hwDW3zgmqylnYD8g9q1Qp4sS+0fHLSqI+YD3oZKVRxu+/Xy7zM1EucRCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b3f36b89aea5ae5517d7c5fab0d600af07a4b4397b5d04a5ff0fa605ebc5a1f","last_reissued_at":"2026-07-05T03:33:36.097794Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:33:36.097794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.08020","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:33:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"le/B2dObR5fkvj3rzqxAMZVL8PNphfdBAZkkl9P4GYXT+qrs3xwzW5xiMCg/y6jqTWNS7HRJOcBjGR3v9elcCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T09:04:45.515282Z"},"content_sha256":"9f63a827664f56cbcdfa998d28823121f56697af9bde46ae58d603e99443bea1","schema_version":"1.0","event_id":"sha256:9f63a827664f56cbcdfa998d28823121f56697af9bde46ae58d603e99443bea1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:FM7TNOE25JNOKUL5PRP2WDLABL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Selective inference for effect modification via the lasso","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Ashkan Ertefaie, Dylan S. Small, Qingyuan Zhao","submitted_at":"2017-05-22T21:57:28Z","abstract_excerpt":"Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters from the treatment effect of interest and use machine learning algorithms to estimate the nuisance pa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08020","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1705.08020/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T03:33:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JsIV5YwfIuu5pjI6Tq2XiXoldbAXQdgeNS/9ewm5QodqcfeNzRh8U2RsRyLgJE51mo9mAFefOc5Wt0FegHNjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T09:04:45.515811Z"},"content_sha256":"c562473d1cc3284a7edca178171722a2487ef29ba6a7372e12375bd7c80109ce","schema_version":"1.0","event_id":"sha256:c562473d1cc3284a7edca178171722a2487ef29ba6a7372e12375bd7c80109ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FM7TNOE25JNOKUL5PRP2WDLABL/bundle.json","state_url":"https://pith.science/pith/FM7TNOE25JNOKUL5PRP2WDLABL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FM7TNOE25JNOKUL5PRP2WDLABL/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":"2026-08-06T09:04:45Z","links":{"resolver":"https://pith.science/pith/FM7TNOE25JNOKUL5PRP2WDLABL","bundle":"https://pith.science/pith/FM7TNOE25JNOKUL5PRP2WDLABL/bundle.json","state":"https://pith.science/pith/FM7TNOE25JNOKUL5PRP2WDLABL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FM7TNOE25JNOKUL5PRP2WDLABL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FM7TNOE25JNOKUL5PRP2WDLABL","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":"89d1f63ca50e5da0fde97b479e27a4adfcc79d074a5568228601f50fbcae1ac7","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-05-22T21:57:28Z","title_canon_sha256":"c2930e5a110f46fe2aa961233536cb58db94275e7ea663b7787c5c0438e13191"},"schema_version":"1.0","source":{"id":"1705.08020","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08020","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08020v4","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08020","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"pith_short_12","alias_value":"FM7TNOE25JNO","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"pith_short_16","alias_value":"FM7TNOE25JNOKUL5","created_at":"2026-07-05T03:33:36Z"},{"alias_kind":"pith_short_8","alias_value":"FM7TNOE2","created_at":"2026-07-05T03:33:36Z"}],"graph_snapshots":[{"event_id":"sha256:c562473d1cc3284a7edca178171722a2487ef29ba6a7372e12375bd7c80109ce","target":"graph","created_at":"2026-07-05T03:33:36Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1705.08020/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters from the treatment effect of interest and use machine learning algorithms to estimate the nuisance pa","authors_text":"Ashkan Ertefaie, Dylan S. Small, Qingyuan Zhao","cross_cats":["math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-05-22T21:57:28Z","title":"Selective inference for effect modification via the lasso"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08020","kind":"arxiv","version":4},"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":"sha256:9f63a827664f56cbcdfa998d28823121f56697af9bde46ae58d603e99443bea1","target":"record","created_at":"2026-07-05T03:33:36Z","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":"89d1f63ca50e5da0fde97b479e27a4adfcc79d074a5568228601f50fbcae1ac7","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-05-22T21:57:28Z","title_canon_sha256":"c2930e5a110f46fe2aa961233536cb58db94275e7ea663b7787c5c0438e13191"},"schema_version":"1.0","source":{"id":"1705.08020","kind":"arxiv","version":4}},"canonical_sha256":"2b3f36b89aea5ae5517d7c5fab0d600af07a4b4397b5d04a5ff0fa605ebc5a1f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b3f36b89aea5ae5517d7c5fab0d600af07a4b4397b5d04a5ff0fa605ebc5a1f","first_computed_at":"2026-07-05T03:33:36.097794Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:33:36.097794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jM5IxyW15MjYC0A8UTKGDZLIoVss+hwDW3zgmqylnYD8g9q1Qp4sS+0fHLSqI+YD3oZKVRxu+/Xy7zM1EucRCw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:33:36.098274Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.08020","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9f63a827664f56cbcdfa998d28823121f56697af9bde46ae58d603e99443bea1","sha256:c562473d1cc3284a7edca178171722a2487ef29ba6a7372e12375bd7c80109ce"],"state_sha256":"007fc3dbcd44d5b7c1b74efdd339d37a6bd74470c55163debec51860c223b6ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8ZNsPsG+t/XfwhjhWtir9jgQ9KfIoTcz7z5z6OqpPO+c1QA3f8MJdh8KqVZc1XemOBmuMj2YEKjAMLAX22AwAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T09:04:45.519636Z","bundle_sha256":"543c0b73781919e507da01a37982b04d67dc36c46b6de7023c175e906c755279"}}