{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DUSAX5CKHKW6OFIT65LZ2TZ37E","short_pith_number":"pith:DUSAX5CK","canonical_record":{"source":{"id":"2506.13680","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T16:37:20Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"937728e894e0d7d09f10d664614a5accfd36052673b17a3c21153b004c8581de","abstract_canon_sha256":"71773d5da2f18e23d6c2b3a67f8b9eac1a1aa35e3b1bad492aef0d008aff85c2"},"schema_version":"1.0"},"canonical_sha256":"1d240bf44a3aade71513f7579d4f3bf917d13578f001dceb2235286ec37a85e4","source":{"kind":"arxiv","id":"2506.13680","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13680","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13680v2","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13680","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"pith_short_12","alias_value":"DUSAX5CKHKW6","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"pith_short_16","alias_value":"DUSAX5CKHKW6OFIT","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"pith_short_8","alias_value":"DUSAX5CK","created_at":"2026-07-07T02:17:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DUSAX5CKHKW6OFIT65LZ2TZ37E","target":"record","payload":{"canonical_record":{"source":{"id":"2506.13680","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T16:37:20Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"937728e894e0d7d09f10d664614a5accfd36052673b17a3c21153b004c8581de","abstract_canon_sha256":"71773d5da2f18e23d6c2b3a67f8b9eac1a1aa35e3b1bad492aef0d008aff85c2"},"schema_version":"1.0"},"canonical_sha256":"1d240bf44a3aade71513f7579d4f3bf917d13578f001dceb2235286ec37a85e4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:17:08.240565Z","signature_b64":"jCpLUyowO5vBHUt8mcAbiIf3L3CDyluBEKUQB3SQRBgrWyr1wi52ZqN0pmxH3S2MWlFZoD1sZYV3zS0EwAV1Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d240bf44a3aade71513f7579d4f3bf917d13578f001dceb2235286ec37a85e4","last_reissued_at":"2026-07-07T02:17:08.239743Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:17:08.239743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.13680","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":"2026-07-07T02:17:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FMU2EwXam0wlg0UoQpvWEoRwBnLyr3LPlWVe72Ac7k8KpPwtHdzBvaOTrEgvMbWD5twy7Qt2u8eTeJXKS2pkAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:36:07.645017Z"},"content_sha256":"e0228e5013a37e91451bd46118d90d7da6a1174cea3313ef9e80910838ac37a8","schema_version":"1.0","event_id":"sha256:e0228e5013a37e91451bd46118d90d7da6a1174cea3313ef9e80910838ac37a8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DUSAX5CKHKW6OFIT65LZ2TZ37E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"cs.LG","authors_text":"Ahmed Alaa, Lars van der Laan, Zhongyuan Liang","submitted_at":"2025-06-16T16:37:20Z","abstract_excerpt":"Estimating conditional average treatment effects (CATE) from observational data involves modeling decisions that differ from supervised learning, particularly concerning how to regularize model complexity. Previous approaches can be grouped into two primary \"meta-learner\" paradigms that impose distinct inductive biases. Indirect meta-learners first fit and regularize separate potential outcome (PO) models and then estimate CATE by taking their difference, whereas direct meta-learners construct and directly regularize estimators for the CATE function itself. Neither approach consistently outper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13680","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.13680/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":"2026-07-07T02:17:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UgAisYgiLoqXxt/IvN3vQQJClTYLw4drh9PzErWEvOeYpVU7780xTR1hSAdDOnTP7byLaNAmCLqdjk+/fDL7BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:36:07.645998Z"},"content_sha256":"b9d67887aad6e425d3dbd29f933c9ff73223481090395361cf16005f62284217","schema_version":"1.0","event_id":"sha256:b9d67887aad6e425d3dbd29f933c9ff73223481090395361cf16005f62284217"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DUSAX5CKHKW6OFIT65LZ2TZ37E/bundle.json","state_url":"https://pith.science/pith/DUSAX5CKHKW6OFIT65LZ2TZ37E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DUSAX5CKHKW6OFIT65LZ2TZ37E/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":"2026-08-05T15:36:07Z","links":{"resolver":"https://pith.science/pith/DUSAX5CKHKW6OFIT65LZ2TZ37E","bundle":"https://pith.science/pith/DUSAX5CKHKW6OFIT65LZ2TZ37E/bundle.json","state":"https://pith.science/pith/DUSAX5CKHKW6OFIT65LZ2TZ37E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DUSAX5CKHKW6OFIT65LZ2TZ37E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DUSAX5CKHKW6OFIT65LZ2TZ37E","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":"71773d5da2f18e23d6c2b3a67f8b9eac1a1aa35e3b1bad492aef0d008aff85c2","cross_cats_sorted":["stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T16:37:20Z","title_canon_sha256":"937728e894e0d7d09f10d664614a5accfd36052673b17a3c21153b004c8581de"},"schema_version":"1.0","source":{"id":"2506.13680","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13680","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13680v2","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13680","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"pith_short_12","alias_value":"DUSAX5CKHKW6","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"pith_short_16","alias_value":"DUSAX5CKHKW6OFIT","created_at":"2026-07-07T02:17:08Z"},{"alias_kind":"pith_short_8","alias_value":"DUSAX5CK","created_at":"2026-07-07T02:17:08Z"}],"graph_snapshots":[{"event_id":"sha256:b9d67887aad6e425d3dbd29f933c9ff73223481090395361cf16005f62284217","target":"graph","created_at":"2026-07-07T02:17:08Z","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/2506.13680/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Estimating conditional average treatment effects (CATE) from observational data involves modeling decisions that differ from supervised learning, particularly concerning how to regularize model complexity. Previous approaches can be grouped into two primary \"meta-learner\" paradigms that impose distinct inductive biases. Indirect meta-learners first fit and regularize separate potential outcome (PO) models and then estimate CATE by taking their difference, whereas direct meta-learners construct and directly regularize estimators for the CATE function itself. Neither approach consistently outper","authors_text":"Ahmed Alaa, Lars van der Laan, Zhongyuan Liang","cross_cats":["stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T16:37:20Z","title":"Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13680","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:e0228e5013a37e91451bd46118d90d7da6a1174cea3313ef9e80910838ac37a8","target":"record","created_at":"2026-07-07T02:17:08Z","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":"71773d5da2f18e23d6c2b3a67f8b9eac1a1aa35e3b1bad492aef0d008aff85c2","cross_cats_sorted":["stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T16:37:20Z","title_canon_sha256":"937728e894e0d7d09f10d664614a5accfd36052673b17a3c21153b004c8581de"},"schema_version":"1.0","source":{"id":"2506.13680","kind":"arxiv","version":2}},"canonical_sha256":"1d240bf44a3aade71513f7579d4f3bf917d13578f001dceb2235286ec37a85e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1d240bf44a3aade71513f7579d4f3bf917d13578f001dceb2235286ec37a85e4","first_computed_at":"2026-07-07T02:17:08.239743Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-07T02:17:08.239743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jCpLUyowO5vBHUt8mcAbiIf3L3CDyluBEKUQB3SQRBgrWyr1wi52ZqN0pmxH3S2MWlFZoD1sZYV3zS0EwAV1Bg==","signature_status":"signed_v1","signed_at":"2026-07-07T02:17:08.240565Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.13680","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e0228e5013a37e91451bd46118d90d7da6a1174cea3313ef9e80910838ac37a8","sha256:b9d67887aad6e425d3dbd29f933c9ff73223481090395361cf16005f62284217"],"state_sha256":"9e4c44c1ec759da8f68786b3492a008a46955820389c8aed55a8a9429ba8b42a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/3nqBHRqYCWZeOx6pM56BUO5pEc9blXnJlmUdJz4WlRQKYiFKMBTRl15ELMyHdSAiawg1kA/XrrmGXhhozMZAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T15:36:07.654379Z","bundle_sha256":"26608a7d3bbfff683717dd2139c6ac4ead7c9f559bf28ec617d12b5e05697255"}}