{"id":"https://openalex.org/W4391801085","doi":"https://doi.org/10.48550/arxiv.2402.07419","title":"Conditional Generative Models are Sufficient to Sample from Any Causal\n Effect Estimand","display_name":"Conditional Generative Models are Sufficient to Sample from Any Causal\n Effect Estimand","publication_year":2024,"publication_date":"2024-02-12","ids":{"openalex":"https://openalex.org/W4391801085","doi":"https://doi.org/10.48550/arxiv.2402.07419"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.07419","pdf_url":"http://arxiv.org/pdf/2402.07419","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2402.07419","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100660922","display_name":"Md. Musfiqur Rahman","orcid":"https://orcid.org/0000-0003-0884-6847"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahman, Md Musfiqur","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039535109","display_name":"Matt Jordan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jordan, Matt","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075323827","display_name":"Murat Kocaoglu","orcid":"https://orcid.org/0000-0003-2447-2689"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kocaoglu, Murat","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":83},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.8475,"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":0.8475,"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/sample","display_name":"Sample (material)","score":0.51091}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.65391207},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.51936466},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.51091},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.40984115},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23425862},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.19106168},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.19045556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13436797},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.07419","pdf_url":"http://arxiv.org/pdf/2402.07419","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.07419","pdf_url":"http://arxiv.org/pdf/2402.07419","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4390718435","https://openalex.org/W4390549206","https://openalex.org/W3137171911","https://openalex.org/W2931662336","https://openalex.org/W2765597752","https://openalex.org/W2748952813","https://openalex.org/W2380075625","https://openalex.org/W2134894512","https://openalex.org/W2077865380","https://openalex.org/W2053487507"],"abstract_inverted_index":{"Causal":[0],"inference":[1],"from":[2,133,170],"observational":[3,35],"data":[4,88],"has":[5],"recently":[6],"found":[7],"many":[8,23],"applications":[9],"in":[10,42],"machine":[11],"learning.":[12],"While":[13],"sound":[14],"and":[15,66,159,166,199],"complete":[16],"algorithms":[17,26],"exist":[18],"to":[19,30,40,79,131],"compute":[20],"causal":[21,61,84,105,110],"effects,":[22],"of":[24,72,118,175,196,202],"these":[25,73],"require":[27],"explicit":[28],"access":[29],"conditional":[31,94,119,182],"likelihoods":[32],"over":[33],"the":[34,43,57,156,160,189,194,200],"distribution,":[36],"which":[37],"is":[38],"difficult":[39],"estimate":[41],"high-dimensional":[44],"regime,":[45],"such":[46,82],"as":[47,83,164],"with":[48,63,89],"images.":[49],"To":[50,141],"alleviate":[51],"this":[52,98,124],"issue,":[53],"researchers":[54],"have":[55],"approached":[56],"problem":[58],"by":[59,192],"simulating":[60],"relations":[62],"neural":[64],"models":[65,184],"obtained":[67],"impressive":[68],"results.":[69],"However,":[70],"none":[71],"existing":[74],"approaches":[75],"can":[76,112],"be":[77,113],"applied":[78],"generic":[80],"scenarios":[81],"graphs":[85],"on":[86,123,138,149,188],"image":[87,139],"latent":[90],"confounders,":[91],"or":[92],"obtain":[93,167],"interventional":[95,136,168],"samples.":[96],"In":[97],"paper,":[99],"we":[100,126,146,178],"show":[101],"that":[102,185],"any":[103,134],"identifiable":[104],"effect":[106],"given":[107],"an":[108,173],"arbitrary":[109],"graph":[111],"computed":[114],"through":[115],"push-forward":[116],"computations":[117],"generative":[120,183],"models.":[121],"Based":[122],"result,":[125],"devise":[127],"a":[128,150],"diffusion-based":[129],"approach":[130],"sample":[132],"(conditional)":[135],"distribution":[137],"data.":[140],"showcase":[142],"our":[143,176],"algorithm's":[144],"performance,":[145],"conduct":[147],"experiments":[148],"Colored":[151],"MNIST":[152],"dataset":[153,191],"having":[154],"both":[155],"treatment":[157],"($X$)":[158],"target":[161],"variables":[162],"($Y$)":[163],"images":[165],"samples":[169],"$P(y|do(x))$.":[171],"As":[172],"application":[174],"algorithm,":[177],"evaluate":[179],"two":[180],"large":[181],"are":[186],"pre-trained":[187],"CelebA":[190],"analyzing":[193],"strength":[195],"spurious":[197],"correlations":[198],"level":[201],"disentanglement":[203],"they":[204],"achieve.":[205]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4391801085","counts_by_year":[],"updated_date":"2025-01-08T22:56:39.005444","created_date":"2024-02-14"}