{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:R3BN77DCQHYXORRCGN5MAVP5NJ","short_pith_number":"pith:R3BN77DC","schema_version":"1.0","canonical_sha256":"8ec2dffc6281f1774622337ac055fd6a43895bb97c6a6653725b0726dfd84577","source":{"kind":"arxiv","id":"2208.00872","version":3},"attestation_state":"computed","paper":{"title":"Towards R-learner with Continuous Treatments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Dehan Kong, Shu Yang, Yichi Zhang","submitted_at":"2022-08-01T14:03:38Z","abstract_excerpt":"The R-learner is widely used in causal inference due to its flexibility and efficiency in estimating the conditional average treatment effect. However, extending the R-learner framework from binary to continuous treatments introduces a non-identifiability issue, as the functional zero constraint inherent to the conditional average treatment effect cannot be directly imposed in the R-loss under continuous treatments. To address this, we propose a two-step identification strategy: we first identify an intermediary function via Tikhonov regularization, and then recover the conditional average tre"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2208.00872","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2022-08-01T14:03:38Z","cross_cats_sorted":[],"title_canon_sha256":"2f8afba47501257de89140b3b1a664ebde54746dec57e50db59b49f11dce8342","abstract_canon_sha256":"7f2d4f45aacd095254fc4b0ced765cb91bef42739473b797bf57d3c0a63d4be3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:39.567736Z","signature_b64":"8z5uJu3qfGARg01GmCGkArX3NFrH5DHQy+riUsAq+YYx/P8tSyQVEYaCC4WzbTszhtxrp85Oz1gTqoqt7MBiAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ec2dffc6281f1774622337ac055fd6a43895bb97c6a6653725b0726dfd84577","last_reissued_at":"2026-07-05T10:50:39.567148Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:39.567148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards R-learner with Continuous Treatments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Dehan Kong, Shu Yang, Yichi Zhang","submitted_at":"2022-08-01T14:03:38Z","abstract_excerpt":"The R-learner is widely used in causal inference due to its flexibility and efficiency in estimating the conditional average treatment effect. However, extending the R-learner framework from binary to continuous treatments introduces a non-identifiability issue, as the functional zero constraint inherent to the conditional average treatment effect cannot be directly imposed in the R-loss under continuous treatments. To address this, we propose a two-step identification strategy: we first identify an intermediary function via Tikhonov regularization, and then recover the conditional average tre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00872","kind":"arxiv","version":3},"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/2208.00872/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2208.00872","created_at":"2026-07-05T10:50:39.567227+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.00872v3","created_at":"2026-07-05T10:50:39.567227+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00872","created_at":"2026-07-05T10:50:39.567227+00:00"},{"alias_kind":"pith_short_12","alias_value":"R3BN77DCQHYX","created_at":"2026-07-05T10:50:39.567227+00:00"},{"alias_kind":"pith_short_16","alias_value":"R3BN77DCQHYXORRC","created_at":"2026-07-05T10:50:39.567227+00:00"},{"alias_kind":"pith_short_8","alias_value":"R3BN77DC","created_at":"2026-07-05T10:50:39.567227+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29076","citing_title":"Doubly cross-fit debiased machine learning of heterogeneous treatment effects under principal stratification","ref_index":56,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ","json":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ.json","graph_json":"https://pith.science/api/pith-number/R3BN77DCQHYXORRCGN5MAVP5NJ/graph.json","events_json":"https://pith.science/api/pith-number/R3BN77DCQHYXORRCGN5MAVP5NJ/events.json","paper":"https://pith.science/paper/R3BN77DC"},"agent_actions":{"view_html":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ","download_json":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ.json","view_paper":"https://pith.science/paper/R3BN77DC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.00872&json=true","fetch_graph":"https://pith.science/api/pith-number/R3BN77DCQHYXORRCGN5MAVP5NJ/graph.json","fetch_events":"https://pith.science/api/pith-number/R3BN77DCQHYXORRCGN5MAVP5NJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ/action/storage_attestation","attest_author":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ/action/author_attestation","sign_citation":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ/action/citation_signature","submit_replication":"https://pith.science/pith/R3BN77DCQHYXORRCGN5MAVP5NJ/action/replication_record"}},"created_at":"2026-07-05T10:50:39.567227+00:00","updated_at":"2026-07-05T10:50:39.567227+00:00"}