{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5XNWVIVUZ445QIZG42QCIMTGIX","short_pith_number":"pith:5XNWVIVU","schema_version":"1.0","canonical_sha256":"eddb6aa2b4cf39d82326e6a024326645ec84a42908b3ca84287b380ecdb0bc58","source":{"kind":"arxiv","id":"1912.03277","version":3},"attestation_state":"computed","paper":{"title":"Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Amit Sharma, Chenhao Tan, Divyat Mahajan","submitted_at":"2019-12-06T18:16:29Z","abstract_excerpt":"To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of such examples. For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world. We formulate the problem of feasibility as prese"},"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":"1912.03277","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-06T18:16:29Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"d0a1808e4a873feab1f03ae48e812f36340477dc1bcef08d315fc1ef7911158c","abstract_canon_sha256":"52348bc93013e249feae2b0337bd9003345e5799853a9565303d6b151c627e17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:09:56.865778Z","signature_b64":"KTA4EBEqtNT8p4fqHmfSDaEofvQude1R4e/PBJ2/gLfBWfXWpUa5BgoqqFfBs74BUeqt+JdVCResGqUIwZP8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eddb6aa2b4cf39d82326e6a024326645ec84a42908b3ca84287b380ecdb0bc58","last_reissued_at":"2026-07-05T01:09:56.865373Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:09:56.865373Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Amit Sharma, Chenhao Tan, Divyat Mahajan","submitted_at":"2019-12-06T18:16:29Z","abstract_excerpt":"To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of such examples. For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world. We formulate the problem of feasibility as prese"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.03277","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/1912.03277/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":"1912.03277","created_at":"2026-07-05T01:09:56.865429+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.03277v3","created_at":"2026-07-05T01:09:56.865429+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.03277","created_at":"2026-07-05T01:09:56.865429+00:00"},{"alias_kind":"pith_short_12","alias_value":"5XNWVIVUZ445","created_at":"2026-07-05T01:09:56.865429+00:00"},{"alias_kind":"pith_short_16","alias_value":"5XNWVIVUZ445QIZG","created_at":"2026-07-05T01:09:56.865429+00:00"},{"alias_kind":"pith_short_8","alias_value":"5XNWVIVU","created_at":"2026-07-05T01:09:56.865429+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01306","citing_title":"PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11373","citing_title":"Causal Algorithmic Recourse: Foundations and Methods","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08030","citing_title":"From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX","json":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX.json","graph_json":"https://pith.science/api/pith-number/5XNWVIVUZ445QIZG42QCIMTGIX/graph.json","events_json":"https://pith.science/api/pith-number/5XNWVIVUZ445QIZG42QCIMTGIX/events.json","paper":"https://pith.science/paper/5XNWVIVU"},"agent_actions":{"view_html":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX","download_json":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX.json","view_paper":"https://pith.science/paper/5XNWVIVU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.03277&json=true","fetch_graph":"https://pith.science/api/pith-number/5XNWVIVUZ445QIZG42QCIMTGIX/graph.json","fetch_events":"https://pith.science/api/pith-number/5XNWVIVUZ445QIZG42QCIMTGIX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX/action/storage_attestation","attest_author":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX/action/author_attestation","sign_citation":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX/action/citation_signature","submit_replication":"https://pith.science/pith/5XNWVIVUZ445QIZG42QCIMTGIX/action/replication_record"}},"created_at":"2026-07-05T01:09:56.865429+00:00","updated_at":"2026-07-05T01:09:56.865429+00:00"}