{"id":"https://openalex.org/W4281489197","doi":"https://doi.org/10.48550/arxiv.2205.11486","title":"Robust and Agnostic Learning of Conditional Distributional Treatment Effects","display_name":"Robust and Agnostic Learning of Conditional Distributional Treatment Effects","publication_year":2022,"publication_date":"2022-05-23","ids":{"openalex":"https://openalex.org/W4281489197","doi":"https://doi.org/10.48550/arxiv.2205.11486"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2205.11486","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.11486","pdf_url":"https://arxiv.org/pdf/2205.11486","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2205.11486","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036921114","display_name":"Nathan Kallus","orcid":"https://orcid.org/0000-0003-1672-0507"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kallus, Nathan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087369029","display_name":"Miruna Oprescu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oprescu, Miruna","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036921114"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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":0.9987999796867371,"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":0.9987999796867371,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9613999724388123,"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":0.9553999900817871,"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/quantile","display_name":"Quantile","score":0.8038139343261719},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.7368447780609131},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.7138293981552124},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.6806402802467346},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5200421810150146},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.5169986486434937},{"id":"https://openalex.org/keywords/conditional-expectation","display_name":"Conditional expectation","score":0.5061387419700623},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3847714960575104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3730999231338501},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35017699003219604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24443978071212769}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.8038139343261719},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.7368447780609131},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.7138293981552124},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6806402802467346},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5200421810150146},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.5169986486434937},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.5061387419700623},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3847714960575104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3730999231338501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35017699003219604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24443978071212769}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2205.11486","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.11486","pdf_url":"https://arxiv.org/pdf/2205.11486","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:RePEc:arx:papers:2205.11486","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"preprint"},{"id":"doi:10.48550/arxiv.2205.11486","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2205.11486","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2205.11486","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.11486","pdf_url":"https://arxiv.org/pdf/2205.11486","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"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/W4206511378","https://openalex.org/W4206618949","https://openalex.org/W2526321210","https://openalex.org/W3205863630","https://openalex.org/W4318833145","https://openalex.org/W2364275385","https://openalex.org/W4388704167","https://openalex.org/W2007977664","https://openalex.org/W4376874882","https://openalex.org/W2224749288"],"abstract_inverted_index":{"The":[0],"conditional":[1,68,103,113,117,122],"average":[2],"treatment":[3,36,49,61,73,115,119,123],"effect":[4,50],"(CATE)":[5],"is":[6,42,134,152,171],"the":[7,18,22,47,102,159,165,196,210],"best":[8,160],"measure":[9],"of":[10,109,162,206,212,224],"individual":[11],"causal":[12],"effects":[13,124,227],"given":[14,129],"baseline":[15],"covariates.":[16],"However,":[17],"CATE":[19],"only":[20],"captures":[21],"(conditional)":[23],"average,":[24],"and":[25,29,75,97,121,141,199],"can":[26,65,157,187],"overlook":[27],"risks":[28],"tail":[30,58],"events,":[31],"which":[32],"are":[33],"important":[34],"to":[35,85],"choice.":[37],"In":[38],"aggregate":[39],"analyses,":[40],"this":[41,80],"usually":[43],"addressed":[44],"by":[45,130],"measuring":[46],"distributional":[48],"(DTE),":[51],"such":[52],"as":[53,217,219],"differences":[54],"in":[55,71,154,173,215,220],"quantiles":[56],"or":[57,87],"expectations":[59],"between":[60],"groups.":[62],"Hypothetically,":[63],"one":[64],"similarly":[66],"fit":[67],"quantile":[69,114],"regressions":[70],"each":[72],"group":[74],"take":[76],"their":[77],"difference,":[78],"but":[79],"would":[81],"not":[82],"be":[83],"robust":[84,96,172],"misspecification":[86],"provide":[88,93,158],"agnostic":[89],"best-in-class":[90],"predictions.":[91],"We":[92,208],"a":[94,107,138,221],"new":[95],"model-agnostic":[98,153],"methodology":[99],"for":[100,106],"learning":[101],"DTE":[104],"(CDTE)":[105],"class":[108,197],"problems":[110],"that":[111,155,174,193],"includes":[112],"effects,":[116,120],"super-quantile":[118],"on":[125,136,144,195,203,228],"coherent":[126],"risk":[127],"measures":[128],"$f$-divergences.":[131],"Our":[132,150,169],"method":[133,151,170],"based":[135],"constructing":[137],"special":[139],"pseudo-outcome":[140],"regressing":[142],"it":[143,156],"covariates":[145],"using":[146],"any":[147],"regression":[148,166],"learner.":[149],"projection":[161],"CDTE":[163],"onto":[164],"model":[167],"class.":[168],"even":[175,200],"if":[176],"we":[177,186],"learn":[178,189],"these":[179],"nuisances":[180],"nonparametrically":[181],"at":[182,191],"very":[183],"slow":[184],"rates,":[185],"still":[188],"CDTEs":[190],"rates":[192],"depend":[194],"complexity":[198],"conduct":[201],"inferences":[202],"linear":[204],"projections":[205],"CDTEs.":[207],"investigate":[209],"behavior":[211],"our":[213],"proposal":[214],"simulations,":[216],"well":[218],"case":[222],"study":[223],"401(k)":[225],"eligibility":[226],"wealth.":[229]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
