{"id":"https://openalex.org/W7162630989","doi":"https://doi.org/10.48550/arxiv.2605.27567","title":"Why LLMs Fail at Causal Discovery and How Interventional Agents Escape","display_name":"Why LLMs Fail at Causal Discovery and How Interventional Agents Escape","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162630989","doi":"https://doi.org/10.48550/arxiv.2605.27567"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27567","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.27567","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137215058","display_name":"Amartya Roy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roy, Amartya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025828990","display_name":"Sonali Parbhoo","orcid":"https://orcid.org/0000-0001-8400-3732"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parbhoo, Sonali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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":0.6481000185012817,"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":0.6481000185012817,"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":0.055399999022483826,"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/T10028","display_name":"Topic Modeling","score":0.046799998730421066,"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/oracle","display_name":"Oracle","score":0.5339999794960022},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.47049999237060547},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4472000002861023},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4453999996185303},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.4406000077724457},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.43160000443458557},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42399999499320984},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41999998688697815}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5562000274658203},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5430999994277954},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4537000060081482},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4472000002861023},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4453999996185303},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3855000138282776},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.34459999203681946},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C2780665704","wikidata":"https://www.wikidata.org/wiki/Q959298","display_name":"Intervention (counseling)","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2597000002861023},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27567","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.27567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27567","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6679249405860901}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Causal":[0,123],"discovery":[1],"is":[2,50,108],"a":[3,100,128,189],"cornerstone":[4],"of":[5],"scientific":[6],"reasoning,":[7],"yet":[8],"whether":[9],"large":[10],"language":[11,130],"models":[12,27],"can":[13],"perform":[14],"it":[15],"reliably":[16],"remains":[17,175],"an":[18,134,144],"open":[19],"question.":[20],"Recent":[21],"benchmarks":[22],"show":[23],"that":[24,63,105],"even":[25],"fine-tuned":[26,181],"plateau":[28],"on":[29],"simple":[30],"causal":[31,67],"graphs":[32,68,152],"and":[33,57,73,205],"degrade":[34],"as":[35,99,133],"complexity":[36],"grows,":[37],"but":[38],"why":[39],"they":[40],"fail":[41],"has":[42],"not":[43],"been":[44],"established.":[45],"We":[46,96,120],"prove":[47],"the":[48,80,88,106,111,158,162,165,172,209],"failure":[49],"fundamental:":[51],"supervised":[52],"fine-tuning,":[53],"direct":[54],"preference":[55,206],"optimization,":[56,207],"in-context":[58],"learning":[59,112],"all":[60],"produce":[61],"predictors":[62],"cannot":[64],"distinguish":[65],"between":[66],"generating":[69],"similar":[70],"observational":[71],"data,":[72],"any":[74,115,184],"attempt":[75],"to":[76,84,110,193],"do":[77],"so":[78],"requires":[79],"model's":[81],"internal":[82],"representations":[83],"grow":[85],"unboundedly,":[86],"violating":[87],"very":[89],"conditions":[90],"under":[91],"which":[92],"these":[93],"methods":[94],"work.":[95],"formalize":[97],"this":[98],"kernel":[101],"obstruction":[102,166],"theorem,":[103],"establishing":[104],"limitation":[107],"intrinsic":[109],"paradigm,":[113],"\\emph{not":[114],"particular":[116],"model":[117,131,174],"or":[118],"dataset}.":[119],"propose":[121],"Agentic":[122],"Bayesian":[124,146],"Optimization":[125],"(A-CBO),":[126],"wherein":[127],"frozen":[129],"serves":[132],"interventional":[135],"oracle":[136],"answering":[137],"targeted":[138],"queries":[139],"about":[140],"intervention":[141],"effects,":[142],"while":[143,171],"external":[145],"loop":[147],"concentrates":[148],"beliefs":[149],"over":[150],"candidate":[151],"in":[153],"logarithmically":[154],"many":[155],"rounds.":[156],"Because":[157],"decision":[159],"operates":[160],"outside":[161],"space":[163],"where":[164],"applies,":[167],"A-CBO":[168,179,200],"provably":[169],"converges":[170],"underlying":[173],"unchanged.":[176],"On":[177,186],"Corr2Cause,":[178,188],"matches":[180],"baselines":[182],"without":[183],"training.":[185],"Extended":[187],"new":[190],"benchmark":[191],"scaling":[192],"24":[194],"variables":[195],"with":[196,208],"18K":[197],"test":[198],"samples,":[199],"significantly":[201],"outperforms":[202],"both":[203],"fine-tuning":[204],"advantage":[210],"growing":[211]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-29T00:00:00"}