{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SPQ4T6E3DUZ2WHHIS7I6PJ4QUE","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":"5f3c886528a2377ab86a9ce8f7efb5ce879e90bcd4e8a41b91a678ba2b21712b","cross_cats_sorted":["econ.EM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-05T14:05:44Z","title_canon_sha256":"e067be00d96b0d22cf694a2b3095172120109560058fb8c99e5f4a16f70efd6a"},"schema_version":"1.0","source":{"id":"2507.04044","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.04044","created_at":"2026-07-05T11:32:41Z"},{"alias_kind":"arxiv_version","alias_value":"2507.04044v1","created_at":"2026-07-05T11:32:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.04044","created_at":"2026-07-05T11:32:41Z"},{"alias_kind":"pith_short_12","alias_value":"SPQ4T6E3DUZ2","created_at":"2026-07-05T11:32:41Z"},{"alias_kind":"pith_short_16","alias_value":"SPQ4T6E3DUZ2WHHI","created_at":"2026-07-05T11:32:41Z"},{"alias_kind":"pith_short_8","alias_value":"SPQ4T6E3","created_at":"2026-07-05T11:32:41Z"}],"graph_snapshots":[{"event_id":"sha256:5409b2a437aa2940525b2a60eb04df57118215432c470981d344d32958141e00","target":"graph","created_at":"2026-07-05T11:32:41Z","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/2507.04044/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Estimation and inference of treatment effects under unconfounded treatment assignments often suffer from bias and the `curse of dimensionality' due to the nonparametric estimation of nuisance parameters for high-dimensional confounders. Although debiased state-of-the-art methods have been proposed for binary treatments under particular treatment models, they can be unstable for small sample sizes. Moreover, directly extending them to general treatment models can lead to computational complexity. We propose a balanced neural networks weighting method for general treatment models, which leverage","authors_text":"Meilin Wang, Wei Huang, Zeqi Wu, Zheng Zhang","cross_cats":["econ.EM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-05T14:05:44Z","title":"A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.04044","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":"sha256:7f1050d312f44ff9dfe3ee1cd18dee5308e867deb5505cba18eb17c27cf66a7a","target":"record","created_at":"2026-07-05T11:32:41Z","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":"5f3c886528a2377ab86a9ce8f7efb5ce879e90bcd4e8a41b91a678ba2b21712b","cross_cats_sorted":["econ.EM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-05T14:05:44Z","title_canon_sha256":"e067be00d96b0d22cf694a2b3095172120109560058fb8c99e5f4a16f70efd6a"},"schema_version":"1.0","source":{"id":"2507.04044","kind":"arxiv","version":1}},"canonical_sha256":"93e1c9f89b1d33ab1ce897d1e7a790a1107899ad1fd5dbd77738962d9d9d2ff6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"93e1c9f89b1d33ab1ce897d1e7a790a1107899ad1fd5dbd77738962d9d9d2ff6","first_computed_at":"2026-07-05T11:32:41.301989Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:32:41.301989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yKrUD74JT7fnkioTvBf23YyKjmJQcPjIqZ1yTzuxPA8/L4qgOFoRSOw2bjzWqZ5WacG7zyLIMdKOorBXfFIHDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:32:41.302623Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.04044","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f1050d312f44ff9dfe3ee1cd18dee5308e867deb5505cba18eb17c27cf66a7a","sha256:5409b2a437aa2940525b2a60eb04df57118215432c470981d344d32958141e00"],"state_sha256":"11421d52e058fbe1daf57c60690414fc2b4432bde373697fa138f46e6121d3dd"}