{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KW5627INXGVA23LAZIDEET4JWX","short_pith_number":"pith:KW5627IN","canonical_record":{"source":{"id":"2505.20536","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-26T21:45:43Z","cross_cats_sorted":["cs.LG","econ.EM","stat.ME"],"title_canon_sha256":"dc942edcf36808f567e17b468c61041d76c2bc9bfdb61119d26d0e88038e3692","abstract_canon_sha256":"f12ba6353effee7e708427c0dbde986b9375344591e8e09a0b2f45b59401b2e0"},"schema_version":"1.0"},"canonical_sha256":"55bbed7d0db9aa0d6d60ca06424f89b5f9e065eb99c44bc47f5cf35c1ec8cdf6","source":{"kind":"arxiv","id":"2505.20536","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.20536","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"arxiv_version","alias_value":"2505.20536v1","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20536","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"pith_short_12","alias_value":"KW5627INXGVA","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"pith_short_16","alias_value":"KW5627INXGVA23LA","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"pith_short_8","alias_value":"KW5627IN","created_at":"2026-07-05T11:10:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KW5627INXGVA23LAZIDEET4JWX","target":"record","payload":{"canonical_record":{"source":{"id":"2505.20536","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-26T21:45:43Z","cross_cats_sorted":["cs.LG","econ.EM","stat.ME"],"title_canon_sha256":"dc942edcf36808f567e17b468c61041d76c2bc9bfdb61119d26d0e88038e3692","abstract_canon_sha256":"f12ba6353effee7e708427c0dbde986b9375344591e8e09a0b2f45b59401b2e0"},"schema_version":"1.0"},"canonical_sha256":"55bbed7d0db9aa0d6d60ca06424f89b5f9e065eb99c44bc47f5cf35c1ec8cdf6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:14.269100Z","signature_b64":"uAmDvpjHXdsOTGE1MwCX7NDWm48pygH6hDQ9X1KqijfSu4ompPEqxxkzmvvjGIbshlnCLo/cuudSCiOwThnZCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55bbed7d0db9aa0d6d60ca06424f89b5f9e065eb99c44bc47f5cf35c1ec8cdf6","last_reissued_at":"2026-07-05T11:10:14.268539Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:14.268539Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.20536","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":"2026-07-05T11:10:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IzPjCgDmP4hIdY3fHkQ8DDiof1fVX63DqMnFwnPI+1qm9D9Ekg74ySZDYI3mD2o94z1aD7ujH2EBryv0zSS/Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T14:11:56.980591Z"},"content_sha256":"970e85ddfacbe1f28e0ea82e86b16e1925b7283e374a6da0cc5949f8cea74a21","schema_version":"1.0","event_id":"sha256:970e85ddfacbe1f28e0ea82e86b16e1925b7283e374a6da0cc5949f8cea74a21"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KW5627INXGVA23LAZIDEET4JWX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","econ.EM","stat.ME"],"primary_cat":"stat.ML","authors_text":"Guanhao Zhou, Xiufan Yu, Yuefeng Han","submitted_at":"2025-05-26T21:45:43Z","abstract_excerpt":"This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) for panel data models, that employs flexible model structures and powerful neural network architectures to cohesively deal with the underlying heterogeneity and nonlinearity of both panel units and covariate effects. The proposed CoDEAL integrates nonlinear covariate effect components (parameterized by a feed-forward neural network) with nonlinear factor structures (modeled by a multi-out"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20536","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/2505.20536/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":"2026-07-05T11:10:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AZyAvUWiPyNpUND1p+BZkIMwnqkFB5eGyJTvt9JVSc5jHDY7y/ZyO2PbUziSmjEC9A7AJ7A73ZyEkK5VCsJ3Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T14:11:56.981112Z"},"content_sha256":"9a21e66efa1b5510972aa0ebbc9319b6b2872810f55b7442f33ae6c1c9ad672f","schema_version":"1.0","event_id":"sha256:9a21e66efa1b5510972aa0ebbc9319b6b2872810f55b7442f33ae6c1c9ad672f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KW5627INXGVA23LAZIDEET4JWX/bundle.json","state_url":"https://pith.science/pith/KW5627INXGVA23LAZIDEET4JWX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KW5627INXGVA23LAZIDEET4JWX/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":"2026-08-09T14:11:56Z","links":{"resolver":"https://pith.science/pith/KW5627INXGVA23LAZIDEET4JWX","bundle":"https://pith.science/pith/KW5627INXGVA23LAZIDEET4JWX/bundle.json","state":"https://pith.science/pith/KW5627INXGVA23LAZIDEET4JWX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KW5627INXGVA23LAZIDEET4JWX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KW5627INXGVA23LAZIDEET4JWX","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":"f12ba6353effee7e708427c0dbde986b9375344591e8e09a0b2f45b59401b2e0","cross_cats_sorted":["cs.LG","econ.EM","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-26T21:45:43Z","title_canon_sha256":"dc942edcf36808f567e17b468c61041d76c2bc9bfdb61119d26d0e88038e3692"},"schema_version":"1.0","source":{"id":"2505.20536","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.20536","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"arxiv_version","alias_value":"2505.20536v1","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20536","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"pith_short_12","alias_value":"KW5627INXGVA","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"pith_short_16","alias_value":"KW5627INXGVA23LA","created_at":"2026-07-05T11:10:14Z"},{"alias_kind":"pith_short_8","alias_value":"KW5627IN","created_at":"2026-07-05T11:10:14Z"}],"graph_snapshots":[{"event_id":"sha256:9a21e66efa1b5510972aa0ebbc9319b6b2872810f55b7442f33ae6c1c9ad672f","target":"graph","created_at":"2026-07-05T11:10:14Z","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/2505.20536/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) for panel data models, that employs flexible model structures and powerful neural network architectures to cohesively deal with the underlying heterogeneity and nonlinearity of both panel units and covariate effects. The proposed CoDEAL integrates nonlinear covariate effect components (parameterized by a feed-forward neural network) with nonlinear factor structures (modeled by a multi-out","authors_text":"Guanhao Zhou, Xiufan Yu, Yuefeng Han","cross_cats":["cs.LG","econ.EM","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-26T21:45:43Z","title":"Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20536","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":"sha256:970e85ddfacbe1f28e0ea82e86b16e1925b7283e374a6da0cc5949f8cea74a21","target":"record","created_at":"2026-07-05T11:10:14Z","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":"f12ba6353effee7e708427c0dbde986b9375344591e8e09a0b2f45b59401b2e0","cross_cats_sorted":["cs.LG","econ.EM","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-26T21:45:43Z","title_canon_sha256":"dc942edcf36808f567e17b468c61041d76c2bc9bfdb61119d26d0e88038e3692"},"schema_version":"1.0","source":{"id":"2505.20536","kind":"arxiv","version":1}},"canonical_sha256":"55bbed7d0db9aa0d6d60ca06424f89b5f9e065eb99c44bc47f5cf35c1ec8cdf6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"55bbed7d0db9aa0d6d60ca06424f89b5f9e065eb99c44bc47f5cf35c1ec8cdf6","first_computed_at":"2026-07-05T11:10:14.268539Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:10:14.268539Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uAmDvpjHXdsOTGE1MwCX7NDWm48pygH6hDQ9X1KqijfSu4ompPEqxxkzmvvjGIbshlnCLo/cuudSCiOwThnZCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:10:14.269100Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.20536","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:970e85ddfacbe1f28e0ea82e86b16e1925b7283e374a6da0cc5949f8cea74a21","sha256:9a21e66efa1b5510972aa0ebbc9319b6b2872810f55b7442f33ae6c1c9ad672f"],"state_sha256":"ab6bd57f937cb79da71cfce7fcb1739abd70659b6ad5979ff5287bfad079fae1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ne4hMxDwlCt7O+8JF9gsY/NrKw0G13G10Tp98XhKAKYs/SeeRtPUE6ziKiLZT4zgT4Lt2HTuEr7y5gpenNwrAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T14:11:56.986058Z","bundle_sha256":"aae7ff4b60579ae77ff29bf80546d14d72798758e3ab10ff817438abd0a9a3b3"}}