{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:B5KGOMKT54QBDS7I6M2HXVLLFT","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":"b2c3c47b45b2dcd0103849b5624aefe88e5f271a1a9861eb6cc1966023ad1d2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T08:41:25Z","title_canon_sha256":"6a96553b94dd6f675359e06a355764a35fcf5db6c03d0a92a5540d92f70a71ab"},"schema_version":"1.0","source":{"id":"2606.03332","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.03332","created_at":"2026-06-03T01:05:55Z"},{"alias_kind":"arxiv_version","alias_value":"2606.03332v1","created_at":"2026-06-03T01:05:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.03332","created_at":"2026-06-03T01:05:55Z"},{"alias_kind":"pith_short_12","alias_value":"B5KGOMKT54QB","created_at":"2026-06-03T01:05:55Z"},{"alias_kind":"pith_short_16","alias_value":"B5KGOMKT54QBDS7I","created_at":"2026-06-03T01:05:55Z"},{"alias_kind":"pith_short_8","alias_value":"B5KGOMKT","created_at":"2026-06-03T01:05:55Z"}],"graph_snapshots":[{"event_id":"sha256:9479d47d8b5bbc42e55bb65cedb3fca18757dfb408692fca925290e5af40f779","target":"graph","created_at":"2026-06-03T01:05:55Z","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/2606.03332/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ often lead to high bias and variance. We propose a principled framework for deriving task-specific strictly proper scoring rules by matching the local curvature of the downstream error metric. We apply this to the Average Treatment Effect (ATE) estimation, deriving a closed-form loss and its correspo","authors_text":"Alexandre Perez-Lebel, Antoine Saillenfest, Ga\\\"el Varoquaux, Marine Le Morvan, Matthieu Labeau, Roman Plaud, Thomas Bonald","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T08:41:25Z","title":"Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.03332","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:b5ca214ea392e424d183e1f23d584501039c8d06450242ad6e439fda19435d9a","target":"record","created_at":"2026-06-03T01:05:55Z","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":"b2c3c47b45b2dcd0103849b5624aefe88e5f271a1a9861eb6cc1966023ad1d2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T08:41:25Z","title_canon_sha256":"6a96553b94dd6f675359e06a355764a35fcf5db6c03d0a92a5540d92f70a71ab"},"schema_version":"1.0","source":{"id":"2606.03332","kind":"arxiv","version":1}},"canonical_sha256":"0f54673153ef2011cbe8f3347bd56b2ceba02b4267b01055b826ad94b5d5184e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f54673153ef2011cbe8f3347bd56b2ceba02b4267b01055b826ad94b5d5184e","first_computed_at":"2026-06-03T01:05:55.234718Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:55.234718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"may8oCaNbMQ2IpGH8TOYNUNr968noVbpfZlLKLeMV2GJz8ToPrHpY7GvLVHJtQWCDm4gOJUt60kITl5Qslk+CQ==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:55.235094Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.03332","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b5ca214ea392e424d183e1f23d584501039c8d06450242ad6e439fda19435d9a","sha256:9479d47d8b5bbc42e55bb65cedb3fca18757dfb408692fca925290e5af40f779"],"state_sha256":"a4dc6f188cfdffb3f01f93dce696f972f917a16a71473f330ee212b36df00852"}