{"id":"https://openalex.org/W4405714271","doi":"https://doi.org/10.48550/arxiv.2412.15401","title":"Quantile Mediation Analytics","display_name":"Quantile Mediation Analytics","publication_year":2024,"publication_date":"2024-12-19","ids":{"openalex":"https://openalex.org/W4405714271","doi":"https://doi.org/10.48550/arxiv.2412.15401"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2412.15401","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.15401","pdf_url":"https://arxiv.org/pdf/2412.15401","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2412.15401","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041563539","display_name":"Canyi Chen","orcid":"https://orcid.org/0000-0002-0673-5812","observed_orcids":["https://orcid.org/0000-0002-0673-5812","https://orcid.org/0009-0006-0277-5346"]},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen, Canyi","raw_affiliation_strings":["Department of Biostatistics , University of Michigan , Ann Arbor , Michigan 48104 , U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics , University of Michigan , Ann Arbor , Michigan 48104 , U.S.A","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101590822","display_name":"Yanyun He","orcid":"https://orcid.org/0000-0001-6279-6102","observed_orcids":["https://orcid.org/0000-0001-6279-6102"]},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"He, Yinqiu","raw_affiliation_strings":["Department of Statistics , University of Wisconsin , Madison , Wisconsin 53706 , U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics , University of Wisconsin , Madison , Wisconsin 53706 , U.S.A","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018732669","display_name":"HUIXIA JUDY WANG","orcid":"https://orcid.org/0000-0002-5195-8564","observed_orcids":["https://orcid.org/0000-0002-5195-8564"]},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wang, Huixia J.","raw_affiliation_strings":["Department of Statistics , George Washington University , Washington , DC 20052 , U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics , George Washington University , Washington , DC 20052 , U.S.A","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027989356","display_name":"Gongjun Xu","orcid":"https://orcid.org/0000-0003-4023-5413","observed_orcids":["https://orcid.org/0000-0003-4023-5413"]},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xu, Gongjun","raw_affiliation_strings":["Department of Statistics , University of Michigan , Ann Arbor , Michigan 48104 , U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics , University of Michigan , Ann Arbor , Michigan 48104 , U.S.A","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027524625","display_name":"Peter X.\u2010K. Song","orcid":"https://orcid.org/0000-0001-7881-7182","observed_orcids":["https://orcid.org/0000-0001-7881-7182"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Peter X. -K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":0.9984999895095825,"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":0.9984999895095825,"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":0.00019999999494757503,"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/T14374","display_name":"Statistical Methods in Epidemiology","score":9.999999747378752e-05,"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/counterfactual-framework","display_name":"counterfactual framework","score":0.902999997138977},{"id":"https://openalex.org/keywords/directed-acyclic-graphs","display_name":"directed acyclic graphs","score":0.8510000109672546},{"id":"https://openalex.org/keywords/type-i-error-control","display_name":"type I error control","score":0.8489999771118164},{"id":"https://openalex.org/keywords/lipidomic-biomarkers","display_name":"lipidomic biomarkers","score":0.8140000104904175},{"id":"https://openalex.org/keywords/phthalates","display_name":"phthalates","score":0.8809999823570251},{"id":"https://openalex.org/keywords/early-childhood-obesity","display_name":"early childhood obesity","score":0.9010000228881836},{"id":"https://openalex.org/keywords/body-mass-index","display_name":"body mass index","score":0.8080000281333923}],"concepts":[{"id":"https://openalex.org/C179420905","wikidata":"https://www.wikidata.org/wiki/Q223871","display_name":"Mediation","level":2,"score":0.683281421661377},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.6770647764205933},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.6481066942214966},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4527103006839752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44884783029556274},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32784736156463623},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.18799850344657898},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.1541280746459961},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2412.15401","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.15401","pdf_url":"https://arxiv.org/pdf/2412.15401","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2412.15401","doi":"https://doi.org/10.48550/arxiv.2412.15401","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2412.15401","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2412.15401","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.15401","pdf_url":"https://arxiv.org/pdf/2412.15401","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.428600013256073,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405714271.pdf","grobid_xml":"https://content.openalex.org/works/W4405714271.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2145230721","https://openalex.org/W2742389172","https://openalex.org/W6927077009","https://openalex.org/W4391920069","https://openalex.org/W4294887697","https://openalex.org/W2762275878","https://openalex.org/W7172244146","https://openalex.org/W4406871531","https://openalex.org/W4246125392","https://openalex.org/W3016743040"],"abstract_inverted_index":{"Mediation":[0],"analytics":[1],"help":[2],"examine":[3],"if":[4],"and":[5,93,115,156,179],"how":[6],"an":[7,14,18,28],"intermediate":[8],"variable":[9,16],"mediates":[10],"the":[11,25,34,48,90,122,133,136,168,187,194],"influence":[12,195],"of":[13,20,27,56,95,126,139,190,196,209],"exposure":[15,197],"on":[17,200],"outcome":[19,29],"interest.":[21],"Quantiles,":[22],"rather":[23],"than":[24],"mean,":[26],"are":[30],"scientifically":[31],"relevant":[32],"to":[33,67,88,158,198],"comparison":[35],"among":[36],"specific":[37],"subgroups":[38],"in":[39,47,78,121,183],"practical":[40],"studies.":[41],"Albeit":[42],"some":[43],"empirical":[44],"studies":[45],"available":[46],"literature,":[49],"there":[50],"lacks":[51],"a":[52,83,100,147,160,180],"thorough":[53],"theoretical":[54],"investigation":[55],"quantile-based":[57],"mediation":[58,85,97,141,171,188],"analysis,":[59],"which":[60,127],"hinders":[61],"practitioners":[62],"from":[63],"using":[64],"such":[65],"methods":[66],"answer":[68],"important":[69],"scientific":[70],"questions.":[71],"To":[72,131],"address":[73],"this":[74,79],"significant":[75],"technical":[76],"gap,":[77],"paper,":[80],"we":[81,145,185],"develop":[82],"quantile":[84,96,110,116,170],"analysis":[86,172],"methodology":[87,173],"facilitate":[89],"identification,":[91],"estimation,":[92],"testing":[94],"effects":[98],"under":[99],"hypothesized":[101],"directed":[102],"acyclic":[103],"graph.":[104],"We":[105,166],"establish":[106,146],"two":[107],"key":[108],"estimands,":[109],"natural":[111,117],"direct":[112],"effect":[113,119,142,189],"(qNDE)":[114],"indirect":[118],"(qNIE),":[120],"counterfactual":[123],"framework,":[124],"both":[125,175],"have":[128],"closed-form":[129],"expressions.":[130],"overcome":[132],"issue":[134],"that":[135,152,184],"null":[137],"hypothesis":[138],"no":[140],"is":[143,153],"composite,":[144],"powerful":[148],"adaptive":[149],"bootstrap":[150],"method":[151],"shown":[154],"theoretically":[155],"numerically":[157],"achieve":[159],"proper":[161],"type":[162],"I":[163],"error":[164],"control.":[165],"illustrate":[167],"proposed":[169],"through":[174],"extensive":[176],"simulation":[177],"experiments":[178],"real-world":[181],"dataset":[182],"investigate":[186],"lipidomic":[191],"biomarkers":[192],"for":[193],"phthalates":[199],"early":[201],"childhood":[202],"obesity":[203],"clinically":[204],"diagnosed":[205],"by":[206],"95\\%":[207],"percentile":[208],"body":[210],"mass":[211],"index.":[212]},"counts_by_year":[],"updated_date":"2026-10-09T09:14:12.587421","created_date":"2024-12-24T00:00:00"}
