{"id":"https://openalex.org/W7150791134","doi":"https://doi.org/10.48550/arxiv.2604.02488","title":"Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery","display_name":"Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery","publication_year":2026,"publication_date":"2026-04-02","ids":{"openalex":"https://openalex.org/W7150791134","doi":"https://doi.org/10.48550/arxiv.2604.02488"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02488","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.02488","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133043846","display_name":"Marco Ruiz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruiz, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015124407","display_name":"M. Arana-Catania","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arana-Catania, Miguel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133045725","display_name":"David R. Ardila","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ardila, David R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052413681","display_name":"Rodrigo Ventura","orcid":"https://orcid.org/0000-0002-5655-9562"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ventura, Rodrigo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.6136000156402588,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.6136000156402588,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.08529999852180481,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.07810000330209732,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/risk-assessment","display_name":"Risk assessment","score":0.5128999948501587},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4754999876022339},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.46869999170303345},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.4440999925136566},{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.382099986076355},{"id":"https://openalex.org/keywords/risk-management","display_name":"Risk management","score":0.32739999890327454},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.32600000500679016}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5203999876976013},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.5128999948501587},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4754999876022339},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.41370001435279846},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.382099986076355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3537999987602234},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3357999920845032},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32659998536109924},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.2953999936580658},{"id":"https://openalex.org/C27504089","wikidata":"https://www.wikidata.org/wiki/Q2602080","display_name":"Audit risk","level":3,"score":0.28119999170303345},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2639000117778778},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02488","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.02488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02488","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.80742347240448,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Time-series":[0],"causal":[1,28],"discovery":[2],"methods":[3,79],"rely":[4],"on":[5,108,139],"assumptions":[6,18],"such":[7],"as":[8,41],"stationarity,":[9],"regular":[10],"sampling,":[11],"and":[12,58,71,136,150],"bounded":[13],"temporal":[14],"dependence.":[15],"When":[16],"these":[17],"are":[19,156],"violated,":[20],"structure":[21],"learning":[22],"can":[23,95],"produce":[24],"confident":[25],"but":[26],"misleading":[27],"graphs":[29],"without":[30],"warning.":[31],"We":[32],"introduce":[33],"Causal-Audit,":[34],"a":[35,109,128],"framework":[36,46,169],"that":[37,77],"formalizes":[38],"assumption":[39,52,102],"validation":[40],"calibrated":[42,65],"risk":[43,66,123],"assessment.":[44],"The":[45,91],"computes":[47],"effect-size":[48],"diagnostics":[49],"across":[50],"five":[51],"families":[53,120],"(stationarity,":[54],"irregularity,":[55],"persistence,":[56],"nonlinearity,":[57],"confounding":[59],"proxies),":[60],"aggregates":[61],"them":[62],"into":[63],"four":[64],"scores":[67,124],"with":[68,158],"uncertainty":[69],"intervals,":[70],"applies":[72],"an":[73],"abstention-aware":[74],"decision":[75],"policy":[76],"recommends":[78],"(e.g.,":[80],"PCMCI+,":[81],"VAR-based":[82],"Granger":[83],"causality)":[84],"only":[85],"when":[86],"evidence":[87],"supports":[88],"reliable":[89],"inference.":[90],"semi-automatic":[92],"diagnostic":[93],"stage":[94],"also":[96],"be":[97],"used":[98],"independently":[99],"for":[100],"structured":[101],"auditing":[103],"in":[104,161],"individual":[105],"studies.":[106],"Evaluation":[107],"synthetic":[110],"atlas":[111],"of":[112,167],"500":[113],"data-generating":[114],"processes":[115],"(DGPs)":[116],"spanning":[117],"10":[118],"violation":[119],"demonstrates":[121],"well-calibrated":[122],"(AUROC":[125],">":[126],"0.95),":[127],"62%":[129],"false":[130],"positive":[131],"reduction":[132],"among":[133],"recommended":[134],"datasets,":[135],"78%":[137],"abstention":[138],"severe-violation":[140],"cases.":[141,163],"On":[142],"21":[143],"external":[144],"evaluations":[145],"from":[146],"TimeGraph":[147],"(18":[148],"categories)":[149],"CausalTime":[151],"(3":[152],"domains),":[153],"recommend-or-abstain":[154],"decisions":[155],"consistent":[157],"benchmark":[159],"specifications":[160],"all":[162],"An":[164],"open-source":[165],"implementation":[166],"our":[168],"is":[170],"available.":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-07T00:00:00"}