{"id":"https://openalex.org/W7154010905","doi":"https://doi.org/10.48550/arxiv.2604.08935","title":"A novel hybrid approach for positive-valued DAG learning","display_name":"A novel hybrid approach for positive-valued DAG learning","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154010905","doi":"https://doi.org/10.48550/arxiv.2604.08935"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08935","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.2604.08935","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120230503","display_name":"Yao Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhao, Yao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5120230503"],"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":0.897599995136261,"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":0.897599995136261,"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":0.011800000444054604,"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"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.010200000368058681,"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/multiplicative-function","display_name":"Multiplicative function","score":0.64410001039505},{"id":"https://openalex.org/keywords/directed-acyclic-graph","display_name":"Directed acyclic graph","score":0.4909999966621399},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4853000044822693},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.47690001130104065},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.4700999855995178},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4659000039100647},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.44110000133514404},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.414000004529953},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.3865000009536743}],"concepts":[{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.64410001039505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5884000062942505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5501000285148621},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5461999773979187},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.47690001130104065},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.4700999855995178},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37599998712539673},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2678000032901764},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C67339327","wikidata":"https://www.wikidata.org/wiki/Q1502576","display_name":"Gene regulatory network","level":4,"score":0.2630999982357025},{"id":"https://openalex.org/C189206191","wikidata":"https://www.wikidata.org/wiki/Q222046","display_name":"Genomics","level":4,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08935","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.2604.08935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08935","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Causal":[0],"discovery":[1,172],"from":[2,58],"observational":[3],"data":[4,60,129],"remains":[5],"a":[6,49,104,167],"fundamental":[7],"challenge":[8],"in":[9,151,173],"machine":[10],"learning":[11,53],"and":[12,133,141,153],"statistics,":[13],"particularly":[14],"when":[15],"variables":[16],"represent":[17],"inherently":[18],"positive":[19],"quantities":[20],"such":[21],"as":[22],"gene":[23],"expression":[24],"levels,":[25],"asset":[26],"prices,":[27],"company":[28],"revenues,":[29],"or":[30],"population":[31],"counts,":[32],"which":[33],"often":[34],"follow":[35],"multiplicative":[36],"rather":[37],"than":[38],"additive":[39],"dynamics.":[40],"We":[41],"propose":[42],"the":[43,76,121],"Hybrid":[44],"Moment-Ratio":[45],"Scoring":[46],"(H-MRS)":[47],"algorithm,":[48],"novel":[50],"method":[51,137],"for":[52,73,86,100,149,170],"directed":[54],"acyclic":[55],"graphs":[56],"(DAGs)":[57],"positive-valued":[59,74,174],"by":[61,114],"combining":[62,159],"moment-based":[63],"scoring":[64],"with":[65,103,162],"log-scale":[66,97,160],"regression.":[67],"The":[68,135],"key":[69],"idea":[70],"is":[71,138],"that":[72,158],"variables,":[75],"moment":[77,111,164],"ratio":[78],"$\\frac{\\mathbb{E}[X_j^2]}{\\mathbb{E}[(\\mathbb{E}[X_j":[79],"\\mid":[80],"S])^2]}$":[81],"provides":[82,166],"an":[83],"effective":[84],"criterion":[85],"causal":[87,171],"ordering,":[88],"where":[89],"$S$":[90],"denotes":[91],"candidate":[92],"parent":[93,117],"sets.":[94],"H-MRS":[95],"integrates":[96],"Ridge":[98],"regression":[99],"moment-ratio":[101],"estimation":[102],"greedy":[105],"ordering":[106],"procedure":[107],"based":[108],"on":[109,126],"raw-scale":[110,163],"ratios,":[112],"followed":[113],"Elastic":[115],"Net-based":[116],"selection":[118],"to":[119],"recover":[120],"final":[122],"DAG":[123],"structure.":[124],"Experiments":[125],"synthetic":[127],"log-linear":[128],"demonstrate":[130],"competitive":[131],"precision":[132],"recall.":[134],"proposed":[136],"computationally":[139],"efficient":[140],"naturally":[142],"respects":[143],"positivity":[144],"constraints,":[145],"making":[146],"it":[147],"suitable":[148],"applications":[150],"genomics":[152],"economics.":[154],"These":[155],"results":[156],"suggest":[157],"modeling":[161],"ratios":[165],"practical":[168],"framework":[169],"domains.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-14T00:00:00"}
