{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:K64XJXM2A7GLFPEKIENFYQPU55","short_pith_number":"pith:K64XJXM2","schema_version":"1.0","canonical_sha256":"57b974dd9a07ccb2bc8a411a5c41f4ef6430ec74169c62e0ed9510100ea65073","source":{"kind":"arxiv","id":"2505.21468","version":1},"attestation_state":"computed","paper":{"title":"Causal Posterior Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Antonietta Mira, Simon Dirmeier","submitted_at":"2025-05-27T17:41:21Z","abstract_excerpt":"We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, i.e., models where the evaluation of the likelihood function is intractable or too computationally expensive, but where one can simulate model outputs given parameter values. CPE utilizes a normalizing flow-based (NF) approximation to the posterior distribution which carefully incorporates the conditional dependence structure induced by the graphical representation of the model into the neural network. Thereby it is possible to improve the accuracy of the approximation. We introduce both di"},"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":"2505.21468","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-27T17:41:21Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0f9a808f639e005aa91bb81629c066603a5423f4bf0a534990a6c7b697352f67","abstract_canon_sha256":"491eca51920ea1f996445c9fbd38801c873affde3723646af9550d058fa6c8cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:42.484250Z","signature_b64":"bVRNXtdhx3nuNfpS+Dn5G5iT1DfCTV79vVfGPZseESyTlG7HTlNlGAw3d+oRLIuDrCfzF+dZmwzv7ulyT1EODA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57b974dd9a07ccb2bc8a411a5c41f4ef6430ec74169c62e0ed9510100ea65073","last_reissued_at":"2026-07-05T11:10:42.483769Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:42.483769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Causal Posterior Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Antonietta Mira, Simon Dirmeier","submitted_at":"2025-05-27T17:41:21Z","abstract_excerpt":"We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, i.e., models where the evaluation of the likelihood function is intractable or too computationally expensive, but where one can simulate model outputs given parameter values. CPE utilizes a normalizing flow-based (NF) approximation to the posterior distribution which carefully incorporates the conditional dependence structure induced by the graphical representation of the model into the neural network. Thereby it is possible to improve the accuracy of the approximation. We introduce both di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21468","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/2505.21468/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":"2505.21468","created_at":"2026-07-05T11:10:42.483827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.21468v1","created_at":"2026-07-05T11:10:42.483827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21468","created_at":"2026-07-05T11:10:42.483827+00:00"},{"alias_kind":"pith_short_12","alias_value":"K64XJXM2A7GL","created_at":"2026-07-05T11:10:42.483827+00:00"},{"alias_kind":"pith_short_16","alias_value":"K64XJXM2A7GLFPEK","created_at":"2026-07-05T11:10:42.483827+00:00"},{"alias_kind":"pith_short_8","alias_value":"K64XJXM2","created_at":"2026-07-05T11:10:42.483827+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/K64XJXM2A7GLFPEKIENFYQPU55","json":"https://pith.science/pith/K64XJXM2A7GLFPEKIENFYQPU55.json","graph_json":"https://pith.science/api/pith-number/K64XJXM2A7GLFPEKIENFYQPU55/graph.json","events_json":"https://pith.science/api/pith-number/K64XJXM2A7GLFPEKIENFYQPU55/events.json","paper":"https://pith.science/paper/K64XJXM2"},"agent_actions":{"view_html":"https://pith.science/pith/K64XJXM2A7GLFPEKIENFYQPU55","download_json":"https://pith.science/pith/K64XJXM2A7GLFPEKIENFYQPU55.json","view_paper":"https://pith.science/paper/K64XJXM2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.21468&json=true","fetch_graph":"https://pith.science/api/pith-number/K64XJXM2A7GLFPEKIENFYQPU55/graph.json","fetch_events":"https://pith.science/api/pith-number/K64XJXM2A7GLFPEKIENFYQPU55/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K64XJXM2A7GLFPEKIENFYQPU55/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K64XJXM2A7GLFPEKIENFYQPU55/action/storage_attestation","attest_author":"https://pith.science/pith/K64XJXM2A7GLFPEKIENFYQPU55/action/author_attestation","sign_citation":"https://pith.science/pith/K64XJXM2A7GLFPEKIENFYQPU55/action/citation_signature","submit_replication":"https://pith.science/pith/K64XJXM2A7GLFPEKIENFYQPU55/action/replication_record"}},"created_at":"2026-07-05T11:10:42.483827+00:00","updated_at":"2026-07-05T11:10:42.483827+00:00"}