{"id":"https://openalex.org/W7214744880","doi":"https://doi.org/10.48550/arxiv.2609.32247","title":"Certifying Interventional Agreement Among Observationally Equivalent Causal Models","display_name":"Certifying Interventional Agreement Among Observationally Equivalent Causal Models","publication_year":2026,"publication_date":"2026-09-26","ids":{"openalex":"https://openalex.org/W7214744880","doi":"https://doi.org/10.48550/arxiv.2609.32247"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.32247","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.32247","pdf_url":"https://arxiv.org/pdf/2609.32247","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.32247","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091952767","display_name":"Sourena Khanzadeh","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Khanzadeh, Sourena","raw_affiliation_strings":["Flybits Labs , Creative AI Hub","Toronto Metropolitan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Flybits Labs , Creative AI Hub","institution_ids":[]},{"raw_affiliation_string":"Toronto Metropolitan University","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026896382","display_name":"Daniel Platnick","orcid":"https://orcid.org/0009-0009-1262-9248","observed_orcids":["https://orcid.org/0009-0009-1262-9248"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Platnick, Daniel","raw_affiliation_strings":["Flybits Labs , Creative AI Hub"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Flybits Labs , Creative AI Hub","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086661628","display_name":"Marjan Alirezaie","orcid":"https://orcid.org/0000-0002-4001-2087","observed_orcids":["https://orcid.org/0000-0002-4001-2087"]},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alirezaie, Marjan","raw_affiliation_strings":["Flybits Labs , Creative AI Hub","Toronto Metropolitan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Flybits Labs , Creative AI Hub","institution_ids":[]},{"raw_affiliation_string":"Toronto Metropolitan University","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089691952","display_name":"Hossein Rahnama","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahnama, Hossein","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.7865999937057495,"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":0.7865999937057495,"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":0.15760000050067902,"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/T11010","display_name":"Logic, Reasoning, and Knowledge","score":0.0071000000461936,"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/observational-equivalence","display_name":"observational equivalence","score":0.8830000162124634},{"id":"https://openalex.org/keywords/causal-modeling","display_name":"causal models","score":0.796999990940094},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.375},{"id":"https://openalex.org/keywords/mixed-integer-linear-programming","display_name":"mixed-integer linear programming","score":0.9570000171661377},{"id":"https://openalex.org/keywords/causal-abstraction","display_name":"causal abstraction","score":0.6359999775886536},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.6380000114440918},{"id":"https://openalex.org/keywords/mnist","display_name":"MNIST","score":0.6399999856948853}],"concepts":[{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.6535999774932861},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.5288000106811523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4778999984264374},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C2780665704","wikidata":"https://www.wikidata.org/wiki/Q959298","display_name":"Intervention (counseling)","level":2,"score":0.43070000410079956},{"id":"https://openalex.org/C27415008","wikidata":"https://www.wikidata.org/wiki/Q7256382","display_name":"Psychological intervention","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3750999867916107},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3675999939441681},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C122637931","wikidata":"https://www.wikidata.org/wiki/Q118084","display_name":"Unit (ring theory)","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C2780343955","wikidata":"https://www.wikidata.org/wiki/Q333173","display_name":"Surprise","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.2784999907016754},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2736000120639801},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2736000120639801},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C2780990831","wikidata":"https://www.wikidata.org/wiki/Q319141","display_name":"Conjecture","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.2583000063896179},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2572999894618988},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2551000118255615},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.32247","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.32247","pdf_url":"https://arxiv.org/pdf/2609.32247","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.32247","doi":"https://doi.org/10.48550/arxiv.2609.32247","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.32247","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.32247","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.32247","pdf_url":"https://arxiv.org/pdf/2609.32247","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":"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/W7214744880.pdf","grobid_xml":"https://content.openalex.org/works/W7214744880.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2803305628","https://openalex.org/W4417281341","https://openalex.org/W7167929566","https://openalex.org/W3217151167","https://openalex.org/W7202135743","https://openalex.org/W7172286473","https://openalex.org/W2184322563","https://openalex.org/W4407091800","https://openalex.org/W4415797792","https://openalex.org/W7213883161"],"abstract_inverted_index":{"Observationally":[0],"equivalent":[1],"causal":[2,153,191],"models":[3,43],"can":[4,94,111,249],"still":[5],"disagree":[6,174],"about":[7],"what":[8,202],"happens":[9],"under":[10],"intervention,":[11,220],"because":[12],"interventions":[13,208,245,257],"create":[14],"inputs":[15],"that":[16,75,90,237,247],"never":[17],"occur":[18],"in":[19,156],"observational":[20,92],"data.":[21],"We":[22],"introduce":[23],"Interventional":[24],"Separation":[25],"Selection":[26],"(ISS),":[27],"which":[28,39,157],"repeatedly":[29],"queries":[30],"the":[31,40,46,48,61,64,69,86,107,116,121,133,190,223,229],"true":[32,65,225],"system":[33,66],"with":[34,79,141,175,206],"an":[35,124],"admissible":[36,83,219],"intervention":[37,55,84],"on":[38,81,106,167,178,197,211,246],"surviving":[41],"candidate":[42,239],"disagree,":[44],"discards":[45],"candidates":[47,122],"outcome":[49],"contradicts,":[50],"and":[51,128,213,253],"stops":[52],"once":[53],"no":[54,91],"within":[56,85],"a":[57,88,146,149,232,235],"cost":[58],"bound":[59],"separates":[60],"survivors.":[62],"If":[63],"is":[67,227],"among":[68,228],"candidates,":[70],"this":[71],"stopping":[72,102,134],"condition":[73,103,135],"certifies":[74,201],"every":[76,82,218,238],"survivor":[77],"agrees":[78],"it":[80,99,110],"bound,":[87],"guarantee":[89],"learner":[93],"give,":[95],"however":[96],"much":[97],"data":[98],"sees.":[100],"The":[101],"depends":[104],"only":[105,196],"survivors,":[108],"so":[109],"be":[112,250],"checked":[113,216],"without":[114],"knowing":[115],"truth.":[117],"For":[118],"continuous":[119],"variables":[120],"form":[123],"infinite":[125],"version":[126],"space,":[127],"mixed-integer":[129],"linear":[130],"programs":[131],"decide":[132],"exactly":[136],"over":[137],"all":[138],"of":[139,180,193,260],"it,":[140],"agreement":[142],"holding":[143],"up":[144],"to":[145],"tolerance.":[147],"On":[148],"three-digit":[150],"colored":[151],"MNIST":[152],"abstraction":[154,226,240],"task":[155],"ink":[158],"hue":[159],"tracks":[160],"digit":[161],"size,":[162],"plain":[163],"convolutional":[164],"networks":[165,194],"trained":[166],"examples":[168],"reach":[169],"zero":[170],"held-out":[171],"error,":[172],"yet":[173],"shape-based":[176],"labels":[177,187],"26%":[179],"single-digit":[181],"edits,":[182],"as":[183,185],"often":[184],"hue-based":[186],"do.":[188],"Auditing":[189],"abstractions":[192],"observed":[195],"such":[198],"images,":[199],"ISS":[200],"each":[203,214],"network":[204,233],"perceives":[205],"13.6":[207],"per":[209],"image":[210],"average,":[212],"certificate,":[215],"against":[217],"holds":[221],"whenever":[222],"network's":[224],"candidates.":[230],"When":[231],"bypasses":[234],"unit":[236,248],"relies":[241],"on,":[242],"certificates":[243],"covering":[244],"silently":[251],"void,":[252],"twenty":[254],"random":[255],"validation":[256],"refute":[258],"69%":[259],"them.":[261]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-30T00:00:00"}