{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:KOMQY4Z6GZ3ICUUVPPK26NFVSX","short_pith_number":"pith:KOMQY4Z6","canonical_record":{"source":{"id":"2210.10171","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-10-18T21:25:48Z","cross_cats_sorted":[],"title_canon_sha256":"de934930187198700e9b0ac99ca45d1934e53c5bcfedf7d4501bc432bb76c982","abstract_canon_sha256":"e9e240698822d90178e61b78d48e260d91a1e4e030e80043379656252694c093"},"schema_version":"1.0"},"canonical_sha256":"53990c733e36768152957bd5af34b595dfb7a84a29e63aec775d52941ae22e72","source":{"kind":"arxiv","id":"2210.10171","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.10171","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"arxiv_version","alias_value":"2210.10171v3","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.10171","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"pith_short_12","alias_value":"KOMQY4Z6GZ3I","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"pith_short_16","alias_value":"KOMQY4Z6GZ3ICUUV","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"pith_short_8","alias_value":"KOMQY4Z6","created_at":"2026-07-05T07:38:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:KOMQY4Z6GZ3ICUUVPPK26NFVSX","target":"record","payload":{"canonical_record":{"source":{"id":"2210.10171","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-10-18T21:25:48Z","cross_cats_sorted":[],"title_canon_sha256":"de934930187198700e9b0ac99ca45d1934e53c5bcfedf7d4501bc432bb76c982","abstract_canon_sha256":"e9e240698822d90178e61b78d48e260d91a1e4e030e80043379656252694c093"},"schema_version":"1.0"},"canonical_sha256":"53990c733e36768152957bd5af34b595dfb7a84a29e63aec775d52941ae22e72","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:24.935333Z","signature_b64":"R3TPX5xuCMfgj2fVEkS6qfqLGi31i9aX6P2ibFjrj1V9adNiuY93QjIeURwT8DCe+5lNE7DJWex2lXtTXh4KDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53990c733e36768152957bd5af34b595dfb7a84a29e63aec775d52941ae22e72","last_reissued_at":"2026-07-05T07:38:24.934854Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:24.934854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.10171","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":"2026-07-05T07:38:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mvbmi8fPUTdW0PZxcd3/X26s4csDmEGbeLxbzjWn648lTFWP52ACPBcpF0u/Dn17YrzPGB1m4NFK7FV55Lf8Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:52:53.512708Z"},"content_sha256":"45e7eb1120d61b5c348bd84fe3b6557e1948f1ae8fd08472773061f302e79703","schema_version":"1.0","event_id":"sha256:45e7eb1120d61b5c348bd84fe3b6557e1948f1ae8fd08472773061f302e79703"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:KOMQY4Z6GZ3ICUUVPPK26NFVSX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Doubly-robust and heteroscedasticity-aware sample trimming for causal inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Johan Ugander, Samir Khan","submitted_at":"2022-10-18T21:25:48Z","abstract_excerpt":"A popular method for variance reduction in observational causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This practice has theoretical grounding when the data are homoscedastic and the propensity model is parametric (Yang and Ding, 2018; Crump et al. 2009), but in modern settings where heteroscedastic data are analyzed with non-parametric models, existing theory fails to support current practice. In this work, we address this challenge by developing new methods and theory for sample trimming. Our contributions are three-f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.10171","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2210.10171/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":"2026-07-05T07:38:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4gYOdp0q8VhoXhuFBjZfHgkwS5Sl1ULXr900wh45M037WoTpyJS2gK/MArofuFlwTrICkg86NEFSD6tAElx4Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:52:53.513197Z"},"content_sha256":"804a06ce22452a685694d18c0bf0792248559bae309e3b3ff6a3fe9ef1944d97","schema_version":"1.0","event_id":"sha256:804a06ce22452a685694d18c0bf0792248559bae309e3b3ff6a3fe9ef1944d97"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KOMQY4Z6GZ3ICUUVPPK26NFVSX/bundle.json","state_url":"https://pith.science/pith/KOMQY4Z6GZ3ICUUVPPK26NFVSX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KOMQY4Z6GZ3ICUUVPPK26NFVSX/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":"2026-08-05T00:52:53Z","links":{"resolver":"https://pith.science/pith/KOMQY4Z6GZ3ICUUVPPK26NFVSX","bundle":"https://pith.science/pith/KOMQY4Z6GZ3ICUUVPPK26NFVSX/bundle.json","state":"https://pith.science/pith/KOMQY4Z6GZ3ICUUVPPK26NFVSX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KOMQY4Z6GZ3ICUUVPPK26NFVSX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:KOMQY4Z6GZ3ICUUVPPK26NFVSX","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":"e9e240698822d90178e61b78d48e260d91a1e4e030e80043379656252694c093","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-10-18T21:25:48Z","title_canon_sha256":"de934930187198700e9b0ac99ca45d1934e53c5bcfedf7d4501bc432bb76c982"},"schema_version":"1.0","source":{"id":"2210.10171","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.10171","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"arxiv_version","alias_value":"2210.10171v3","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.10171","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"pith_short_12","alias_value":"KOMQY4Z6GZ3I","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"pith_short_16","alias_value":"KOMQY4Z6GZ3ICUUV","created_at":"2026-07-05T07:38:24Z"},{"alias_kind":"pith_short_8","alias_value":"KOMQY4Z6","created_at":"2026-07-05T07:38:24Z"}],"graph_snapshots":[{"event_id":"sha256:804a06ce22452a685694d18c0bf0792248559bae309e3b3ff6a3fe9ef1944d97","target":"graph","created_at":"2026-07-05T07:38:24Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.10171/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A popular method for variance reduction in observational causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This practice has theoretical grounding when the data are homoscedastic and the propensity model is parametric (Yang and Ding, 2018; Crump et al. 2009), but in modern settings where heteroscedastic data are analyzed with non-parametric models, existing theory fails to support current practice. In this work, we address this challenge by developing new methods and theory for sample trimming. Our contributions are three-f","authors_text":"Johan Ugander, Samir Khan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-10-18T21:25:48Z","title":"Doubly-robust and heteroscedasticity-aware sample trimming for causal inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.10171","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":"sha256:45e7eb1120d61b5c348bd84fe3b6557e1948f1ae8fd08472773061f302e79703","target":"record","created_at":"2026-07-05T07:38:24Z","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":"e9e240698822d90178e61b78d48e260d91a1e4e030e80043379656252694c093","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-10-18T21:25:48Z","title_canon_sha256":"de934930187198700e9b0ac99ca45d1934e53c5bcfedf7d4501bc432bb76c982"},"schema_version":"1.0","source":{"id":"2210.10171","kind":"arxiv","version":3}},"canonical_sha256":"53990c733e36768152957bd5af34b595dfb7a84a29e63aec775d52941ae22e72","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53990c733e36768152957bd5af34b595dfb7a84a29e63aec775d52941ae22e72","first_computed_at":"2026-07-05T07:38:24.934854Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:38:24.934854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R3TPX5xuCMfgj2fVEkS6qfqLGi31i9aX6P2ibFjrj1V9adNiuY93QjIeURwT8DCe+5lNE7DJWex2lXtTXh4KDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:38:24.935333Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.10171","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45e7eb1120d61b5c348bd84fe3b6557e1948f1ae8fd08472773061f302e79703","sha256:804a06ce22452a685694d18c0bf0792248559bae309e3b3ff6a3fe9ef1944d97"],"state_sha256":"950014a31dc8d38d6a64e690353fa24842aabc424e032efdb417d09675b67dab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+NaI/VSQmlU2lBg8pOtzPZSY1VZekHyMHw/2pj4uan8RzZKatt5QOBa0CEOBngvv9ET78nmFWjL7g4emLX3xCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T00:52:53.516716Z","bundle_sha256":"10ecb08a22991e08509f444b99b0b89b6dde7b25d404294b3dd92433e5245e73"}}