{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XGMOW2KQ6IFU7435BU7HZ5YJS4","short_pith_number":"pith:XGMOW2KQ","schema_version":"1.0","canonical_sha256":"b998eb6950f20b4ff37d0d3e7cf709970593e5a2fe38a2e57f9e3bcbc0800a70","source":{"kind":"arxiv","id":"2302.10135","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Luca Castri, Marc Hanheide, Nicola Bellotto, Sariah Mghames","submitted_at":"2023-02-20T18:11:45Z","abstract_excerpt":"Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications. In this paper, we propose an extension of a state-of-the-art causal discovery method, PCMCI, embedding an additional feature-selection module based on transfer entropy. Starting from a prefixed set of variables, the new algorithm reconstructs the causal model of the observed system by considering only its main features and neglecting those deemed unnecessary for understanding the evolution of the system. We first validat"},"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":"2302.10135","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2023-02-20T18:11:45Z","cross_cats_sorted":[],"title_canon_sha256":"0c001e93d24c3cadbc7e3b9826d6fb438e8d98a258de966e0930143ad110f764","abstract_canon_sha256":"ac5c5265a3f410402fab7f8137da571fc3eae02dad2176bcadd5ca11df674e33"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:43:33.574030Z","signature_b64":"viH+kR2+SOSjDXKB1hjpfvgh6mGVWFHOgtEerO7jROBKeJAdso2/ZZ8UazFJyjbL7hdRz0YD0tG5HduJbKHgCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b998eb6950f20b4ff37d0d3e7cf709970593e5a2fe38a2e57f9e3bcbc0800a70","last_reissued_at":"2026-07-05T05:43:33.573558Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:43:33.573558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Luca Castri, Marc Hanheide, Nicola Bellotto, Sariah Mghames","submitted_at":"2023-02-20T18:11:45Z","abstract_excerpt":"Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications. In this paper, we propose an extension of a state-of-the-art causal discovery method, PCMCI, embedding an additional feature-selection module based on transfer entropy. Starting from a prefixed set of variables, the new algorithm reconstructs the causal model of the observed system by considering only its main features and neglecting those deemed unnecessary for understanding the evolution of the system. We first validat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.10135","kind":"arxiv","version":1},"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/2302.10135/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":"2302.10135","created_at":"2026-07-05T05:43:33.573618+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.10135v1","created_at":"2026-07-05T05:43:33.573618+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.10135","created_at":"2026-07-05T05:43:33.573618+00:00"},{"alias_kind":"pith_short_12","alias_value":"XGMOW2KQ6IFU","created_at":"2026-07-05T05:43:33.573618+00:00"},{"alias_kind":"pith_short_16","alias_value":"XGMOW2KQ6IFU7435","created_at":"2026-07-05T05:43:33.573618+00:00"},{"alias_kind":"pith_short_8","alias_value":"XGMOW2KQ","created_at":"2026-07-05T05:43:33.573618+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4","json":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4.json","graph_json":"https://pith.science/api/pith-number/XGMOW2KQ6IFU7435BU7HZ5YJS4/graph.json","events_json":"https://pith.science/api/pith-number/XGMOW2KQ6IFU7435BU7HZ5YJS4/events.json","paper":"https://pith.science/paper/XGMOW2KQ"},"agent_actions":{"view_html":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4","download_json":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4.json","view_paper":"https://pith.science/paper/XGMOW2KQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.10135&json=true","fetch_graph":"https://pith.science/api/pith-number/XGMOW2KQ6IFU7435BU7HZ5YJS4/graph.json","fetch_events":"https://pith.science/api/pith-number/XGMOW2KQ6IFU7435BU7HZ5YJS4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4/action/storage_attestation","attest_author":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4/action/author_attestation","sign_citation":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4/action/citation_signature","submit_replication":"https://pith.science/pith/XGMOW2KQ6IFU7435BU7HZ5YJS4/action/replication_record"}},"created_at":"2026-07-05T05:43:33.573618+00:00","updated_at":"2026-07-05T05:43:33.573618+00:00"}