{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:S7IPR4OFIIMF6C776FNXSYLJWZ","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":"c2e2c1cc369ea48c3e954d831ee4ff8cb59246d78cd0c88b4f60555046970e7e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-02-07T07:04:37Z","title_canon_sha256":"dfd0ea3c350984f605011360ddde1cced60fd2c2f80201e181d488bc6dbdd4a0"},"schema_version":"1.0","source":{"id":"2502.04699","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.04699","created_at":"2026-07-05T10:54:42Z"},{"alias_kind":"arxiv_version","alias_value":"2502.04699v2","created_at":"2026-07-05T10:54:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.04699","created_at":"2026-07-05T10:54:42Z"},{"alias_kind":"pith_short_12","alias_value":"S7IPR4OFIIMF","created_at":"2026-07-05T10:54:42Z"},{"alias_kind":"pith_short_16","alias_value":"S7IPR4OFIIMF6C77","created_at":"2026-07-05T10:54:42Z"},{"alias_kind":"pith_short_8","alias_value":"S7IPR4OF","created_at":"2026-07-05T10:54:42Z"}],"graph_snapshots":[{"event_id":"sha256:18dade281792b4e677c606fcce06185f7ece5aa5e4961f9c9744bbae34c9f204","target":"graph","created_at":"2026-07-05T10:54:42Z","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/2502.04699/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We address the problem of estimating heterogeneous treatment effects in panel data, adopting the popular Difference-in-Differences (DiD) framework under the conditional parallel trends assumption. We propose a novel doubly robust meta-learner for the Conditional Average Treatment Effect on the Treated (CATT), reducing the estimation to a convex risk minimization problem involving a set of auxiliary models. Our framework allows for the flexible estimation of the CATT, when conditioning on any subset of variables of interest using generic machine learning. Leveraging Neyman orthogonality, our pr","authors_text":"Eleanor Dillon, Haoge Chang, Hui Lan, Vasilis Syrgkanis","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-02-07T07:04:37Z","title":"A Meta-learner for Heterogeneous Effects in Difference-in-Differences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.04699","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":"sha256:20864c97f3b0d8bfb08633765bd89b5a10b9653321deb3ac70fe5fbe47c3c17b","target":"record","created_at":"2026-07-05T10:54:42Z","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":"c2e2c1cc369ea48c3e954d831ee4ff8cb59246d78cd0c88b4f60555046970e7e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-02-07T07:04:37Z","title_canon_sha256":"dfd0ea3c350984f605011360ddde1cced60fd2c2f80201e181d488bc6dbdd4a0"},"schema_version":"1.0","source":{"id":"2502.04699","kind":"arxiv","version":2}},"canonical_sha256":"97d0f8f1c542185f0bfff15b796169b65ad18b3bc62a8502224647cf1453df32","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"97d0f8f1c542185f0bfff15b796169b65ad18b3bc62a8502224647cf1453df32","first_computed_at":"2026-07-05T10:54:42.914944Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:54:42.914944Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hxmLd833UaPqI/dJ3ArPgOoRA2X5BO/o9bAm5eRL5ZgdSyT01iqMx8i5G02W3EAKBVSz8gs0tIoLOcb8DWe4Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T10:54:42.915449Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.04699","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:20864c97f3b0d8bfb08633765bd89b5a10b9653321deb3ac70fe5fbe47c3c17b","sha256:18dade281792b4e677c606fcce06185f7ece5aa5e4961f9c9744bbae34c9f204"],"state_sha256":"233772f27d4b68b2809a97887462226f1012bc5f7ec5cc923c90629bd25516b3"}