{"id":"https://openalex.org/W4309801498","doi":"https://doi.org/10.48550/arxiv.2211.11119","title":"Counterfactual Learning with Multioutput Deep Kernels","display_name":"Counterfactual Learning with Multioutput Deep Kernels","publication_year":2022,"publication_date":"2022-11-20","ids":{"openalex":"https://openalex.org/W4309801498","doi":"https://doi.org/10.48550/arxiv.2211.11119"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2211.11119","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.11119","pdf_url":"https://arxiv.org/pdf/2211.11119","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2211.11119","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080813974","display_name":"Alberto Caron","orcid":"https://orcid.org/0000-0001-5643-2302"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Caron, Alberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023729638","display_name":"Gianluca Baio","orcid":"https://orcid.org/0000-0003-4314-2570"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baio, Gianluca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5011826037","display_name":"Ioanna Manolopoulou","orcid":"https://orcid.org/0000-0002-5379-2916"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manolopoulou, Ioanna","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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9663000106811523,"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"}},"topics":[{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9663000106811523,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9445000290870667,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9380999803543091,"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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.959929883480072},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.6554415225982666},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6230348944664001},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6053491234779358},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6013686060905457},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5099910497665405},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4897799491882324},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4585115909576416},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.45605653524398804},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.44558489322662354},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44213423132896423},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.4122607111930847},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.41064295172691345},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21911078691482544},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18044599890708923},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.12051966786384583},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.09528842568397522}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.959929883480072},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6554415225982666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6230348944664001},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6053491234779358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6013686060905457},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5099910497665405},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4897799491882324},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4585115909576416},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.45605653524398804},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.44558489322662354},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44213423132896423},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.4122607111930847},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.41064295172691345},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21911078691482544},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18044599890708923},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.12051966786384583},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.09528842568397522},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2211.11119","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.11119","pdf_url":"https://arxiv.org/pdf/2211.11119","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10178275","is_oa":false,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10178275/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Transactions of Machine Learning Research , 2022 (11) , Article 235. (2022)","raw_type":"Article"},{"id":"doi:10.48550/arxiv.2211.11119","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2211.11119","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2211.11119","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.11119","pdf_url":"https://arxiv.org/pdf/2211.11119","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4372260129","https://openalex.org/W1498469922","https://openalex.org/W4243804444","https://openalex.org/W3215034539","https://openalex.org/W4313422683","https://openalex.org/W3088104152","https://openalex.org/W4282978140","https://openalex.org/W4280530714","https://openalex.org/W2894915327","https://openalex.org/W2161504683"],"abstract_inverted_index":{"In":[0,63],"this":[1],"paper,":[2],"we":[3,70,114],"address":[4],"the":[5,64,68,80,83,95,99,116,119],"challenge":[6],"of":[7,37,67,85,97,101,118],"performing":[8],"counterfactual":[9,38,87],"inference":[10],"with":[11,19,60],"observational":[12],"data":[13],"via":[14,105],"Bayesian":[15],"nonparametric":[16],"regression":[17],"adjustment,":[18],"a":[20,34],"focus":[21],"on":[22,72,122],"high-dimensional":[23],"settings":[24],"featuring":[25],"multiple":[26,29],"actions":[27],"and":[28,47,82,110,133],"correlated":[30],"outcomes.":[31],"We":[32,92],"present":[33],"general":[35],"class":[36],"multi-task":[39],"deep":[40],"kernels":[41],"models":[42],"that":[43,125],"estimate":[44],"causal":[45,102,128],"effects":[46,103,129],"learn":[48],"policies":[49],"proficiently":[50],"thanks":[51],"to":[52,77],"their":[53],"sample":[54],"efficiency":[55],"gains,":[56],"while":[57],"scaling":[58],"well":[59],"high":[61],"dimensions.":[62],"first":[65],"part":[66],"work,":[69],"rely":[71],"Structural":[73],"Causal":[74],"Models":[75],"(SCM)":[76],"formally":[78],"introduce":[79],"setup":[81],"problem":[84],"identifying":[86],"quantities":[88],"under":[89],"observed":[90],"confounding.":[91],"then":[93],"discuss":[94],"benefits":[96],"tackling":[98],"task":[100],"estimation":[104],"stacked":[106],"coregionalized":[107],"Gaussian":[108],"Processes":[109],"Deep":[111],"Kernels.":[112],"Finally,":[113],"demonstrate":[115],"use":[117],"proposed":[120],"methods":[121],"simulated":[123],"experiments":[124],"span":[126],"individual":[127],"estimation,":[130],"off-policy":[131],"evaluation":[132],"optimization.":[134]},"counts_by_year":[],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}