{"id":"https://openalex.org/W4405487848","doi":"https://doi.org/10.4310/sii.241023040813","title":"Variable selection for doubly robust causal inference","display_name":"Variable selection for doubly robust causal inference","publication_year":2024,"publication_date":"2024-12-17","ids":{"openalex":"https://openalex.org/W4405487848","doi":"https://doi.org/10.4310/sii.241023040813","pmid":"https://pubmed.ncbi.nlm.nih.gov/40896297","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/12395465"},"language":"en","primary_location":{"id":"doi:10.4310/sii.241023040813","doi":"https://doi.org/10.4310/sii.241023040813","is_oa":false,"landing_page_url":"https://doi.org/10.4310/sii.241023040813","pdf_url":null,"source":{"id":"https://openalex.org/S2502110258","display_name":"Statistics and Its Interface","issn_l":"1938-7989","issn":["1938-7989","1938-7997"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","jufo-1","norway-1","poland-40","russia-white-list-4","vabb-shw"],"host_organization":"https://openalex.org/P4310316281","host_organization_name":"Lehigh University","host_organization_lineage":["https://openalex.org/P4310316281"],"host_organization_lineage_names":["Lehigh University"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Its Interface","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12395465/pdf/nihms-2081868.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Eunah Cho","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210131320","display_name":"LG (South Korea)","ror":"https://ror.org/03ddh2c27","country_code":"KR","type":"company","lineage":["https://openalex.org/I4210131320"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Eunah Cho","raw_affiliation_strings":["AI/Big Data Analysis Team, LG Display, 245, LG-ro, Wollong-myeon, Paju-si, Gyeonggi-do, The Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AI/Big Data Analysis Team, LG Display, 245, LG-ro, Wollong-myeon, Paju-si, Gyeonggi-do, The Republic of Korea","institution_ids":["https://openalex.org/I4210131320"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001016275","display_name":"Shu Yang","orcid":"https://orcid.org/0000-0001-7703-707X","observed_orcids":["https://orcid.org/0000-0001-7703-707X"]},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shu Yang","raw_affiliation_strings":["Department of Statistics, 2311 Stinson Drive, 5109 SAS Hall, Campus Box 8203, NC State University, Raleigh, North Carolina 27695, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, 2311 Stinson Drive, 5109 SAS Hall, Campus Box 8203, NC State University, Raleigh, North Carolina 27695, United States","institution_ids":["https://openalex.org/I137902535"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210131320"],"apc_list":null,"apc_paid":null,"fwci":1.5356,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.84941813,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"18","issue":"1","first_page":"93","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9911999702453613,"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":0.9911999702453613,"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":0.008100000210106373,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.0,"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/feature-selection","display_name":"feature selection","score":0.9580000042915344},{"id":"https://openalex.org/keywords/doubly-robust-estimation","display_name":"doubly robust estimation","score":0.8180000185966492},{"id":"https://openalex.org/keywords/augmented-inverse-probability-weighting","display_name":"augmented inverse probability weighting","score":0.9819999933242798},{"id":"https://openalex.org/keywords/propensity-score","display_name":"propensity score","score":0.656000018119812},{"id":"https://openalex.org/keywords/confounder-adjustment","display_name":"confounding adjustment","score":0.8980000019073486},{"id":"https://openalex.org/keywords/average-causal-effect","display_name":"average causal effect","score":0.8989999890327454},{"id":"https://openalex.org/keywords/two-stage-procedure","display_name":"two-stage procedure","score":0.5989999771118164},{"id":"https://openalex.org/keywords/observational-studies","display_name":"observational studies","score":0.7630000114440918},{"id":"https://openalex.org/keywords/efficient-estimation","display_name":"efficient estimation","score":0.4320000112056732}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6732332110404968},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6046547293663025},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5529916882514954},{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.4555283188819885},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.42847347259521484},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.426099568605423},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4250980019569397},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.42451217770576477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37545377016067505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23179706931114197},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.4310/sii.241023040813","doi":"https://doi.org/10.4310/sii.241023040813","is_oa":false,"landing_page_url":"https://doi.org/10.4310/sii.241023040813","pdf_url":null,"source":{"id":"https://openalex.org/S2502110258","display_name":"Statistics and Its Interface","issn_l":"1938-7989","issn":["1938-7989","1938-7997"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","jufo-1","norway-1","poland-40","russia-white-list-4","vabb-shw"],"host_organization":"https://openalex.org/P4310316281","host_organization_name":"Lehigh University","host_organization_lineage":["https://openalex.org/P4310316281"],"host_organization_lineage_names":["Lehigh University"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Its Interface","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:12395465","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12395465","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12395465/pdf/nihms-2081868.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Stat Interface","raw_type":"Text"},{"id":"pmid:40896297","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40896297","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and its interface","raw_type":"Journal Article"},{"id":"pmh:oai:arXiv.org:2301.11094","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2301.11094","pdf_url":"https://arxiv.org/pdf/2301.11094","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2301.11094","doi":"https://doi.org/10.48550/arxiv.2301.11094","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2301.11094","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:12395465","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12395465","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12395465/pdf/nihms-2081868.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Stat Interface","raw_type":"Text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G6417518737","display_name":"Spatial Causal Inference for Wildland Fire Smoke Effects on Air Pollution and Health","funder_award_id":"R01ES031651","funder_id":"https://openalex.org/F4320337361","funder_display_name":"National Institute of Environmental Health Sciences"},{"id":"https://openalex.org/G7008060206","display_name":"Empower treatment effects evaluation of randomized clinical trials for elderly patients with integrated real-world data","funder_award_id":"R01AG066883","funder_id":"https://openalex.org/F4320337337","funder_display_name":"National Institute on Aging"}],"funders":[{"id":"https://openalex.org/F4320337337","display_name":"National Institute on Aging","ror":"https://ror.org/049v75w11"},{"id":"https://openalex.org/F4320337361","display_name":"National Institute of Environmental Health Sciences","ror":"https://ror.org/00j4k1h63"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405487848.pdf","grobid_xml":"https://content.openalex.org/works/W4405487848.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W1638078919"],"related_works":["https://openalex.org/W1638138942","https://openalex.org/W1551481736","https://openalex.org/W2278177624","https://openalex.org/W4398192028","https://openalex.org/W3181001361","https://openalex.org/W2173613996","https://openalex.org/W4396844203","https://openalex.org/W3208796639","https://openalex.org/W2066443416","https://openalex.org/W4417006739"],"abstract_inverted_index":{"Confounding":[0],"control":[1,121],"is":[2,67,84,158],"crucial":[3],"and":[4,122,192,226],"yet":[5],"challenging":[6],"for":[7,27,90,95,118,136,154,189,196],"causal":[8,31],"inference":[9],"based":[10],"on":[11,44],"observational":[12],"studies.":[13],"Under":[14],"the":[15,29,40,46,51,61,65,91,106,129,141,150,163,166,172,176,193,200,205,212,216],"typical":[16],"unconfoundness":[17],"assumption,":[18],"augmented":[19],"inverse":[20],"probability":[21],"weighting":[22],"(AIPW)":[23],"has":[24],"been":[25],"popular":[26],"estimating":[28],"average":[30],"effect":[32],"(ACE)":[33],"due":[34],"to":[35,55,70],"its":[36],"double":[37,143,173,207],"robustness":[38,144,174,208],"in":[39],"sense":[41],"it":[42],"relies":[43],"either":[45,162],"propensity":[47,92,151],"score":[48,93,152],"model":[49,54,153],"or":[50,165,168],"outcome":[52,116,167],"mean":[53],"be":[56],"correctly":[57],"specified.":[58],"To":[59],"ensure":[60],"key":[62],"assumption":[63],"holds,":[64],"effort":[66],"often":[68],"made":[69],"collect":[71],"a":[72,100,159,184],"sufficiently":[73],"rich":[74],"set":[75],"of":[76,161,175,215],"pretreatment":[77],"variables,":[78],"rendering":[79],"variable":[80,88,101,133,190,221],"selection":[81,89,134,191,222],"imperative.":[82],"It":[83],"well":[85],"known":[86],"that":[87,128,157],"targeted":[94,135],"accurate":[96],"prediction":[97],"may":[98,139],"produce":[99],"ACE":[102],"estimator":[103,131,195,218],"by":[104],"including":[105],"instrument":[107],"variables.":[108],"Thus,":[109],"many":[110],"recent":[111],"works":[112],"recommend":[113],"selecting":[114],"all":[115],"predictors":[117],"both":[119],"confounding":[120],"efficient":[123,137],"estimation.":[124,197],"This":[125],"article":[126],"shows":[127],"AIPW":[130,177,194,217],"with":[132,187,219],"estimation":[138],"lose":[140],"desirable":[142,206],"property.":[145,209],"Instead,":[146],"we":[147,182],"propose":[148,183],"controlling":[149],"any":[155],"covariate":[156],"predictor":[160],"treatment":[164],"both,":[169],"which":[170],"preserves":[171],"estimator.":[178],"Using":[179],"this":[180],"principle,":[181],"two-stage":[185],"procedure":[186,202],"penalization":[188],"We":[198,210],"show":[199],"proposed":[201],"benefits":[203],"from":[204],"evaluate":[211],"finite-sample":[213],"performance":[214],"various":[220],"criteria":[223],"through":[224],"simulation":[225],"an":[227],"application.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2024-12-18T00:00:00"}
