{"id":"https://openalex.org/W7165378833","doi":"https://doi.org/10.48550/arxiv.2606.19594","title":"Unsupervised Causal Abstractions Discovery","display_name":"Unsupervised Causal Abstractions Discovery","publication_year":2026,"publication_date":"2026-06-17","ids":{"openalex":"https://openalex.org/W7165378833","doi":"https://doi.org/10.48550/arxiv.2606.19594"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.19594","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19594","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.19594","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104435509","display_name":"Th\u00e9o Saulus","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saulus, Th\u00e9o","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051076050","display_name":"Simon Lacoste-Julien","orcid":"https://orcid.org/0000-0001-6485-6180"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lacoste-Julien, Simon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5109398524","display_name":"Dhanya Sridhar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sridhar, Dhanya","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":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":0.9014000296592712,"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":0.9014000296592712,"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":0.03150000050663948,"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":0.007000000216066837,"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/identifiability","display_name":"Identifiability","score":0.8248000144958496},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6811000108718872},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.628600001335144},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4350999891757965},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.40380001068115234},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.3919999897480011}],"concepts":[{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.8248000144958496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7355999946594238},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6811000108718872},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.628600001335144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5697000026702881},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5023999810218811},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3691999912261963},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2718999981880188},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.19594","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19594","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,"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.19594","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19594","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,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Causal":[0],"abstractions":[1],"formalize":[2],"when":[3],"a":[4,15,25,31,51,79,86,101],"high-level":[5,33,52,107],"structural":[6],"causal":[7,64,87],"model":[8,34,53],"(SCM)":[9],"captures":[10],"the":[11,39,46],"interventional":[12],"behavior":[13],"of":[14,20,49],"lower-level":[16],"SCM.":[17,108],"Existing":[18],"applications":[19],"this":[21,106],"notion":[22],"largely":[23],"follow":[24],"hypothesis-testing":[26],"paradigm:":[27],"an":[28],"expert":[29],"proposes":[30],"candidate":[32],"and":[35,66,97],"then":[36],"evaluates":[37],"if":[38],"low-level":[40,56],"system":[41],"implements":[42],"it.":[43],"We":[44],"study":[45],"complementary":[47],"problem":[48],"learning":[50],"directly":[54],"from":[55,62],"measurements.":[57],"Our":[58],"contributions":[59],"leverage":[60],"hypotheses":[61],"low-rank":[63,80],"discovery,":[65],"can":[67],"be":[68],"summarized":[69],"as":[70],"follows:":[71],"(1)":[72],"we":[73,90,99],"show":[74],"that":[75,84],"observations":[76],"generated":[77],"by":[78],"graph":[81],"induce":[82],"latents":[83],"form":[85],"abstraction,":[88],"(2)":[89],"provide":[91],"identifiability":[92],"results":[93],"about":[94],"these":[95],"latents,":[96],"(3)":[98],"propose":[100],"practical":[102],"objective":[103],"to":[104],"learn":[105]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-20T00:00:00"}