{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YFVFFH7T6MBG5H3BG5D5IVXDTR","short_pith_number":"pith:YFVFFH7T","canonical_record":{"source":{"id":"2507.09718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-13T17:37:04Z","cross_cats_sorted":[],"title_canon_sha256":"015c3b65b68f6c6ea719e78f7dc9e6da5cfbca2bdea629719594ef635c74a53d","abstract_canon_sha256":"ccac39fd8210a4b85147ea640c5e21ec5cb2f640aa6b7dce10c6aa861a21bc48"},"schema_version":"1.0"},"canonical_sha256":"c16a529ff3f3026e9f613747d456e39c5b7752085a27abbcc4cc655a46a70af7","source":{"kind":"arxiv","id":"2507.09718","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.09718","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"arxiv_version","alias_value":"2507.09718v1","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09718","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"pith_short_12","alias_value":"YFVFFH7T6MBG","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"pith_short_16","alias_value":"YFVFFH7T6MBG5H3B","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"pith_short_8","alias_value":"YFVFFH7T","created_at":"2026-07-05T11:36:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YFVFFH7T6MBG5H3BG5D5IVXDTR","target":"record","payload":{"canonical_record":{"source":{"id":"2507.09718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-13T17:37:04Z","cross_cats_sorted":[],"title_canon_sha256":"015c3b65b68f6c6ea719e78f7dc9e6da5cfbca2bdea629719594ef635c74a53d","abstract_canon_sha256":"ccac39fd8210a4b85147ea640c5e21ec5cb2f640aa6b7dce10c6aa861a21bc48"},"schema_version":"1.0"},"canonical_sha256":"c16a529ff3f3026e9f613747d456e39c5b7752085a27abbcc4cc655a46a70af7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:38.651032Z","signature_b64":"oqAoooA5ttLRjzZe8JKETjS9C/SB5XeNTxmH/ypN2SmRUwr2WtjgQvf/irQg+qrfbFjr9rhapFnrBj6iPTZ8Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c16a529ff3f3026e9f613747d456e39c5b7752085a27abbcc4cc655a46a70af7","last_reissued_at":"2026-07-05T11:36:38.650510Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:38.650510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.09718","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:36:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AfTQAoPSFgCyCuGJ40IHwzO9rWVElS/tx2OEc0rEJ5/yuNWWHvCjkLhI4+Hj6RxPVyHDx/Z+VJnBU5Dqxft1BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T17:41:49.020595Z"},"content_sha256":"2368f354a2b40cf3aa9c07205e919727ca96ee66d951613fdbe9d3be215be45e","schema_version":"1.0","event_id":"sha256:2368f354a2b40cf3aa9c07205e919727ca96ee66d951613fdbe9d3be215be45e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YFVFFH7T6MBG5H3BG5D5IVXDTR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bridging Structural Causal Inference and Machine Learning The S-DIDML Estimator for Heterogeneous Treatment Effects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Anzhi Xu, Yile Yu","submitted_at":"2025-07-13T17:37:04Z","abstract_excerpt":"In response to the increasing complexity of policy environments and the proliferation of high-dimensional data, this paper introduces the S-DIDML estimator a framework grounded in structure and semiparametrically flexible for causal inference. By embedding Difference-in-Differences (DID) logic within a Double Machine Learning (DML) architecture, the S-DIDML approach combines the strengths of temporal identification, machine learning-based nuisance adjustment, and orthogonalized estimation. We begin by identifying critical limitations in existing methods, including the lack of structural interp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09718","kind":"arxiv","version":1},"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/2507.09718/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":"2026-07-05T11:36:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aULBXqCPNlqkwh2lqmtZDyYrf6L4h5dRK+dywt1egWZLmhedKDIT1sBl2xHN5ShW60ODCnAYsVIpY30AkbxaDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T17:41:49.021125Z"},"content_sha256":"53f5aecc0dc532a0cbf2b4e7712819da2c5000349d1e35eb540b7aeb9325f86d","schema_version":"1.0","event_id":"sha256:53f5aecc0dc532a0cbf2b4e7712819da2c5000349d1e35eb540b7aeb9325f86d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YFVFFH7T6MBG5H3BG5D5IVXDTR/bundle.json","state_url":"https://pith.science/pith/YFVFFH7T6MBG5H3BG5D5IVXDTR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YFVFFH7T6MBG5H3BG5D5IVXDTR/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":"2026-08-08T17:41:49Z","links":{"resolver":"https://pith.science/pith/YFVFFH7T6MBG5H3BG5D5IVXDTR","bundle":"https://pith.science/pith/YFVFFH7T6MBG5H3BG5D5IVXDTR/bundle.json","state":"https://pith.science/pith/YFVFFH7T6MBG5H3BG5D5IVXDTR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YFVFFH7T6MBG5H3BG5D5IVXDTR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YFVFFH7T6MBG5H3BG5D5IVXDTR","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":"ccac39fd8210a4b85147ea640c5e21ec5cb2f640aa6b7dce10c6aa861a21bc48","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-13T17:37:04Z","title_canon_sha256":"015c3b65b68f6c6ea719e78f7dc9e6da5cfbca2bdea629719594ef635c74a53d"},"schema_version":"1.0","source":{"id":"2507.09718","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.09718","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"arxiv_version","alias_value":"2507.09718v1","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09718","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"pith_short_12","alias_value":"YFVFFH7T6MBG","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"pith_short_16","alias_value":"YFVFFH7T6MBG5H3B","created_at":"2026-07-05T11:36:38Z"},{"alias_kind":"pith_short_8","alias_value":"YFVFFH7T","created_at":"2026-07-05T11:36:38Z"}],"graph_snapshots":[{"event_id":"sha256:53f5aecc0dc532a0cbf2b4e7712819da2c5000349d1e35eb540b7aeb9325f86d","target":"graph","created_at":"2026-07-05T11:36:38Z","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/2507.09718/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In response to the increasing complexity of policy environments and the proliferation of high-dimensional data, this paper introduces the S-DIDML estimator a framework grounded in structure and semiparametrically flexible for causal inference. By embedding Difference-in-Differences (DID) logic within a Double Machine Learning (DML) architecture, the S-DIDML approach combines the strengths of temporal identification, machine learning-based nuisance adjustment, and orthogonalized estimation. We begin by identifying critical limitations in existing methods, including the lack of structural interp","authors_text":"Anzhi Xu, Yile Yu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-13T17:37:04Z","title":"Bridging Structural Causal Inference and Machine Learning The S-DIDML Estimator for Heterogeneous Treatment Effects"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09718","kind":"arxiv","version":1},"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":"sha256:2368f354a2b40cf3aa9c07205e919727ca96ee66d951613fdbe9d3be215be45e","target":"record","created_at":"2026-07-05T11:36:38Z","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":"ccac39fd8210a4b85147ea640c5e21ec5cb2f640aa6b7dce10c6aa861a21bc48","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-13T17:37:04Z","title_canon_sha256":"015c3b65b68f6c6ea719e78f7dc9e6da5cfbca2bdea629719594ef635c74a53d"},"schema_version":"1.0","source":{"id":"2507.09718","kind":"arxiv","version":1}},"canonical_sha256":"c16a529ff3f3026e9f613747d456e39c5b7752085a27abbcc4cc655a46a70af7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c16a529ff3f3026e9f613747d456e39c5b7752085a27abbcc4cc655a46a70af7","first_computed_at":"2026-07-05T11:36:38.650510Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:36:38.650510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oqAoooA5ttLRjzZe8JKETjS9C/SB5XeNTxmH/ypN2SmRUwr2WtjgQvf/irQg+qrfbFjr9rhapFnrBj6iPTZ8Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:36:38.651032Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.09718","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2368f354a2b40cf3aa9c07205e919727ca96ee66d951613fdbe9d3be215be45e","sha256:53f5aecc0dc532a0cbf2b4e7712819da2c5000349d1e35eb540b7aeb9325f86d"],"state_sha256":"d3f82bc8586244bcde1d41a23f310c06724d13b4b39acdc52f56f14de7385fb8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gi0HxME7eW2srvRa+xYY3XJZHjtz4QyXizA6u1HAnV5eOrevMVb8VSXxksKthq0NEUnatklDJGyWOjEabjMYDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T17:41:49.024637Z","bundle_sha256":"640d17ed075353b7902adf12415f97c85c04de178fc35f2065da46e7b4470bf2"}}