{"id":"https://openalex.org/W7124175540","doi":"https://doi.org/10.1109/tsmc.2025.3646993","title":"Causality-Informed Neural Networks for Regularized Learning in Regression Problems","display_name":"Causality-Informed Neural Networks for Regularized Learning in Regression Problems","publication_year":2026,"publication_date":"2026-01-14","ids":{"openalex":"https://openalex.org/W7124175540","doi":"https://doi.org/10.1109/tsmc.2025.3646993"},"language":"en","primary_location":{"id":"doi:10.1109/tsmc.2025.3646993","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsmc.2025.3646993","pdf_url":null,"source":{"id":"https://openalex.org/S4210209078","display_name":"IEEE Transactions on Systems Man and Cybernetics Systems","issn_l":"2168-2216","issn":["2168-2216","2168-2232"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://ira.lib.polyu.edu.hk/bitstream/10397/118131/1/Zhang_Causality-informed_Neural_Networks.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080316854","display_name":"Xiaoge Zhang","orcid":"https://orcid.org/0000-0001-6831-3175"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiaoge Zhang","raw_affiliation_strings":["Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-6831-3175","affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123045966","display_name":"Tao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Tao Wang","raw_affiliation_strings":["Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0003-0201-8100","affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123018983","display_name":"Xiao-Lin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Lin Wang","raw_affiliation_strings":["Business School, Sichuan University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-0100-8154","affiliations":[{"raw_affiliation_string":"Business School, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122998726","display_name":"Feng-Lei Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Feng-Lei Fan","raw_affiliation_strings":["Department of Data Science, Frontier of Artificial Networks (FAN) Laboratory, City University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Science, Frontier of Artificial Networks (FAN) Laboratory, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123039371","display_name":"Yiu-Ming Cheung","orcid":null},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yiu-Ming Cheung","raw_affiliation_strings":["Department of Computer Science, Hong Kong Baptist University, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-7629-4648","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Hong Kong Baptist University, Hong Kong, China","institution_ids":["https://openalex.org/I141568987"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112057482","display_name":"Indranil Bose","orcid":null},"institutions":[{"id":"https://openalex.org/I1289910757","display_name":"NEOMA Business School","ror":"https://ror.org/01g1pe685","country_code":"FR","type":"education","lineage":["https://openalex.org/I1289910757"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Indranil Bose","raw_affiliation_strings":["NEOMA Business School, Reims, France"],"raw_orcid":"https://orcid.org/0000-0002-5737-966X","affiliations":[{"raw_affiliation_string":"NEOMA Business School, Reims, France","institution_ids":["https://openalex.org/I1289910757"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06548189,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"56","issue":"3","first_page":"1895","last_page":"1910"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.3273000121116638,"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":0.3273000121116638,"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":0.1421000063419342,"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/T12676","display_name":"Machine Learning and ELM","score":0.04859999939799309,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/directed-acyclic-graph","display_name":"Directed acyclic graph","score":0.8201000094413757},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7114999890327454},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6710000038146973},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6270999908447266},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.5181999802589417},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.4787999987602234},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4041999876499176},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.36629998683929443}],"concepts":[{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.8201000094413757},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7114999890327454},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6710000038146973},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6270999908447266},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5751000046730042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5595999956130981},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.5181999802589417},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.4787999987602234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4212999939918518},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3635999858379364},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C79897977","wikidata":"https://www.wikidata.org/wiki/Q5054568","display_name":"Causal chain","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.29190000891685486},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C164126121","wikidata":"https://www.wikidata.org/wiki/Q766383","display_name":"Quantitative structure\u2013activity relationship","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsmc.2025.3646993","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsmc.2025.3646993","pdf_url":null,"source":{"id":"https://openalex.org/S4210209078","display_name":"IEEE Transactions on Systems Man and Cybernetics Systems","issn_l":"2168-2216","issn":["2168-2216","2168-2232"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","raw_type":"journal-article"},{"id":"pmh:oai:ira.lib.polyu.edu.hk:10397/118131","is_oa":true,"landing_page_url":"http://hdl.handle.net/10397/118131","pdf_url":"http://ira.lib.polyu.edu.hk/bitstream/10397/118131/1/Zhang_Causality-informed_Neural_Networks.pdf","source":{"id":"https://openalex.org/S4306400205","display_name":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I14243506","host_organization_name":"Hong Kong Polytechnic University","host_organization_lineage":["https://openalex.org/I14243506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal/Magazine Article"}],"best_oa_location":{"id":"pmh:oai:ira.lib.polyu.edu.hk:10397/118131","is_oa":true,"landing_page_url":"http://hdl.handle.net/10397/118131","pdf_url":"http://ira.lib.polyu.edu.hk/bitstream/10397/118131/1/Zhang_Causality-informed_Neural_Networks.pdf","source":{"id":"https://openalex.org/S4306400205","display_name":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I14243506","host_organization_name":"Hong Kong Polytechnic University","host_organization_lineage":["https://openalex.org/I14243506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal/Magazine Article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7425025105476379,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1651900536","display_name":null,"funder_award_id":"62406269","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320320955","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322598","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7124175540.pdf","grobid_xml":"https://content.openalex.org/works/W7124175540.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neural":[0],"networks":[1],"that":[2,249,266],"overlook":[3],"the":[4,22,77,82,87,93,129,138,162,172,191,200,211,217,230,238,242,275,282],"underlying":[5],"causal":[6,41,64,90,95,106,118,141,174,214,272,286],"relationships":[7,91,107,215],"among":[8,43,143,216,241],"observed":[9,44,144,159,218],"variables":[10,45,145,160],"pose":[11],"significant":[12],"risks":[13],"in":[14,92,161,199,226,288],"high-stakes":[15],"decision-making":[16],"contexts":[17],"due":[18],"to":[19,50,75,127,236,280],"concerns":[20],"about":[21],"robustness":[23],"and":[24,154,167,186,196,270],"stability":[25],"of":[26,81,89,132,213,232,259,285],"model":[27],"performance.":[28,293],"To":[29],"tackle":[30],"this":[31],"issue,":[32],"we":[33,136,170,228],"present":[34],"a":[35,47,71,123,147,151,180,256],"general":[36],"approach":[37],"for":[38],"embedding":[39],"hierarchical":[40,63,140,173],"structure":[42,65],"into":[46,146,176,274],"neural":[48,59,83,148,276,290],"network":[49,60,84,149,277],"inform":[51],"its":[52],"learning.":[53,134],"The":[54,97],"proposed":[55,98],"methodology,":[56],"termed":[57],"causality-informed":[58],"(CINN),":[61],"exploits":[62],"learned":[66],"from":[67,108],"observational":[68,109],"data":[69,110],"as":[70,122,164,204,221],"structurally":[72],"informed":[73],"prior":[74],"guide":[76],"layer-to-layer":[78],"architectural":[79],"design":[80],"while":[85],"maintaining":[86],"orientation":[88],"discovered":[94,139],"graph.":[96],"method":[99],"involves":[100],"three":[101],"steps.":[102],"First,":[103],"CINN":[104,177,188,208,250],"mines":[105],"via":[111,150],"directed":[112],"acyclic":[113],"graph":[114,142],"(DAG)":[115],"learning,":[116],"where":[117],"discovery":[119],"is":[120,278],"recast":[121],"continuous":[124],"optimization":[125],"problem":[126],"circumvent":[128],"combinatorial":[130],"nature":[131],"DAG":[133,163,175,184,201],"Second,":[135],"encode":[137],"dedicated":[152],"architecture":[153],"loss":[155,192,223],"function.":[156],"By":[157],"classifying":[158],"root,":[165],"intermediate,":[166],"leaf":[168,197],"nodes,":[169],"translate":[171],"by":[178],"creating":[179],"one-to-one":[181],"correspondence":[182],"between":[183],"nodes":[185,198],"certain":[187],"neurons.":[189],"For":[190],"function,":[193],"both":[194],"intermediate":[195],"are":[202],"treated":[203],"target":[205],"outputs":[206],"during":[207],"training,":[209],"facilitating":[210],"co-learning":[212],"variables.":[219],"Finally,":[220],"multiple":[222,243],"components":[224],"emerge":[225],"CINN,":[227],"leverage":[229],"projection":[231],"conflicting":[233],"gradients":[234],"(PCGrads)":[235],"mitigate":[237],"gradient":[239],"interference":[240],"learning":[244],"tasks.":[245],"Computational":[246],"studies":[247],"indicate":[248],"outperforms":[251],"several":[252],"state-of-the-art":[253],"methods":[254],"across":[255],"broad":[257],"range":[258],"datasets.":[260],"In":[261],"addition,":[262],"an":[263],"ablation":[264],"study":[265],"incrementally":[267],"incorporates":[268],"structural":[269],"quantitative":[271],"knowledge":[273,287],"conducted":[279],"highlight":[281],"pivotal":[283],"role":[284],"enhancing":[289],"network\u2019s":[291],"prediction":[292]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-15T00:00:00"}