{"id":"https://openalex.org/W7130725281","doi":"https://doi.org/10.48550/arxiv.2602.17187","title":"Anti-causal domain generalization: Leveraging unlabeled data","display_name":"Anti-causal domain generalization: Leveraging unlabeled data","publication_year":2026,"publication_date":"2026-02-19","ids":{"openalex":"https://openalex.org/W7130725281","doi":"https://doi.org/10.48550/arxiv.2602.17187"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2602.17187","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2602.17187","pdf_url":"https://arxiv.org/pdf/2602.17187","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2602.17187","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052823119","display_name":"Sorawit Saengkyongam","orcid":"https://orcid.org/0000-0003-1581-259X","observed_orcids":["https://orcid.org/0000-0003-1581-259X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saengkyongam, Sorawit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074413937","display_name":"Juan L. Gamella","orcid":"https://orcid.org/0000-0001-9728-1125","observed_orcids":["https://orcid.org/0000-0001-9728-1125"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gamella, Juan L.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126512492","display_name":"Andrew C. Miller","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miller, Andrew C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002127148","display_name":"Jonas Peters","orcid":"https://orcid.org/0000-0002-1487-7511","observed_orcids":["https://orcid.org/0000-0002-1487-7511"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peters, Jonas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113173834","display_name":"Nicolai Meinshausen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meinshausen, Nicolai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5071541590","display_name":"Christina Heinze\u2010Deml","orcid":"https://orcid.org/0000-0002-2489-9415","observed_orcids":["https://orcid.org/0000-0002-2489-9415"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heinze-Deml, Christina","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":true,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9887999892234802,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9887999892234802,"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":0.002099999925121665,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.0015999999595806003,"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/domain-generalization","display_name":"domain generalization","score":0.9639999866485596},{"id":"https://openalex.org/keywords/distribution-shift","display_name":"distribution shift","score":0.7289999723434448},{"id":"https://openalex.org/keywords/unlabeled-data","display_name":"unlabeled data","score":0.8859999775886536},{"id":"https://openalex.org/keywords/covariate-shift","display_name":"covariate shift","score":0.5569999814033508},{"id":"https://openalex.org/keywords/causal-inference","display_name":"causal inference","score":0.41200000047683716}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7263000011444092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5889999866485596},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5627999901771545},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5353999733924866},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5220000147819519},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5060999989509583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4970000088214874},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.37709999084472656},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32690000534057617},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.32100000977516174},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.298799991607666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2937000095844269},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27799999713897705},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2602.17187","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2602.17187","pdf_url":"https://arxiv.org/pdf/2602.17187","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2602.17187","doi":"https://doi.org/10.48550/arxiv.2602.17187","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.17187","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2602.17187","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2602.17187","pdf_url":"https://arxiv.org/pdf/2602.17187","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7130725281.pdf","grobid_xml":"https://content.openalex.org/works/W7130725281.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2953494151","https://openalex.org/W4226029354","https://openalex.org/W4405768214","https://openalex.org/W4416382397","https://openalex.org/W4417295833","https://openalex.org/W6910731448","https://openalex.org/W7106153248"],"related_works":["https://openalex.org/W4313378835","https://openalex.org/W4300978644","https://openalex.org/W2768783441","https://openalex.org/W4405621195","https://openalex.org/W4388998009","https://openalex.org/W2991356716","https://openalex.org/W4396858475","https://openalex.org/W7200033747","https://openalex.org/W4312232143","https://openalex.org/W4395074201"],"abstract_inverted_index":{"The":[0],"problem":[1],"of":[2,116,134,142],"domain":[3,45],"generalization":[4,46],"concerns":[5],"learning":[6],"predictive":[7],"models":[8],"that":[9,63,104,124],"are":[10,38],"robust":[11],"to":[12,70,79,93,109],"distribution":[13],"shifts":[14],"when":[15,35],"deployed":[16],"in":[17,47,111],"new,":[18],"previously":[19],"unseen":[20],"environments.":[21,99,135],"Existing":[22],"methods":[23,103,126],"typically":[24],"require":[25,89],"labeled":[26,36],"data":[27,37,96],"from":[28,97],"multiple":[29,98],"training":[30],"environments,":[31,120],"limiting":[32],"their":[33],"applicability":[34],"scarce.":[39],"In":[40],"this":[41,59],"work,":[42],"we":[43,137],"study":[44],"an":[48],"anti-causal":[49],"setting,":[50],"where":[51],"the":[52,55,65,71,76,106,112,117,139],"outcome":[53],"causes":[54],"observed":[56],"covariates.":[57],"Under":[58],"structure,":[60],"environment":[61],"perturbations":[62],"affect":[64],"covariates":[66,118],"do":[67],"not":[68,88],"propagate":[69],"outcome,":[72],"which":[73],"motivates":[74],"regularizing":[75],"model's":[77,107],"sensitivity":[78,108],"these":[80,84,125],"perturbations.":[81],"Crucially,":[82],"estimating":[83],"perturbation":[85],"directions":[86],"does":[87],"labels,":[90],"enabling":[91],"us":[92],"leverage":[94],"unlabeled":[95],"We":[100],"propose":[101],"two":[102],"penalize":[105],"variations":[110],"mean":[113],"and":[114,122,150],"covariance":[115],"across":[119],"respectively,":[121],"prove":[123],"have":[127],"worst-case":[128],"optimality":[129],"guarantees":[130],"under":[131],"certain":[132],"classes":[133],"Finally,":[136],"demonstrate":[138],"empirical":[140],"performance":[141],"our":[143],"approach":[144],"on":[145],"a":[146,151],"controlled":[147],"physical":[148],"system":[149],"physiological":[152],"signal":[153],"dataset.":[154]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-02-21T00:00:00"}