{"id":"https://openalex.org/W60695964","doi":null,"title":"Statistical Inference when using Data Adaptive Estimators of Nuisance Parameters","display_name":"Statistical Inference when using Data Adaptive Estimators of Nuisance Parameters","publication_year":2012,"publication_date":"2012-01-01","ids":{"openalex":"https://openalex.org/W60695964","mag":"60695964"},"language":"en","primary_location":{"id":"pmh:oai:biostats.bepress.com:ucbbiostat-1305","doi":null,"is_oa":true,"landing_page_url":"https://biostats.bepress.com/ucbbiostat/paper302","pdf_url":"https://biostats.bepress.com/cgi/viewcontent.cgi?article=1305&context=ucbbiostat","source":{"id":"https://openalex.org/S4377196124","display_name":"Collection of Biostatistics Research Archive","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"U.C. Berkeley Division of Biostatistics Working Paper Series","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://biostats.bepress.com/cgi/viewcontent.cgi?article=1305&context=ucbbiostat","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038659463","display_name":"van der P Paul Laan","orcid":"https://orcid.org/0000-0002-8778-2302","observed_orcids":["https://orcid.org/0000-0002-8778-2302"]},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"van der P Paul Laan","raw_affiliation_strings":["University of California, Berkeley"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Berkeley","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":null,"display_name":"J.W. Mark","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"J.W. Mark","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8648,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73676988,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"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":0.9937000274658203,"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":0.9937000274658203,"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":0.0027000000700354576,"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":0.001500000013038516,"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/targeted-maximum-likelihood-estimation","display_name":"targeted maximum likelihood estimation","score":0.8939999938011169},{"id":"https://openalex.org/keywords/super-learning","display_name":"super learning","score":0.765999972820282},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.9259999990463257},{"id":"https://openalex.org/keywords/nuisance-parameter-estimation","display_name":"nuisance parameter estimation","score":0.8809999823570251},{"id":"https://openalex.org/keywords/propensity-score","display_name":"propensity score","score":0.7329999804496765},{"id":"https://openalex.org/keywords/treatment-effect-estimation","display_name":"treatment effect estimation","score":0.5559999942779541},{"id":"https://openalex.org/keywords/bias-variance-trade-off","display_name":"bias-variance trade-off","score":0.7179999947547913},{"id":"https://openalex.org/keywords/asymptotic-linearity","display_name":"asymptotic linearity","score":0.9070000052452087},{"id":"https://openalex.org/keywords/influence-function","display_name":"influence function","score":0.6460000276565552},{"id":"https://openalex.org/keywords/inverse-probability-of-treatment-weighting","display_name":"inverse probability of treatment weighting","score":0.8920000195503235},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.6800000071525574},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"statistical inference","score":0.609000027179718}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.8462643623352051},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.7499243021011353},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5924428105354309},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5530336499214172},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5247143507003784},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.5033482909202576},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.4754973351955414},{"id":"https://openalex.org/C68805675","wikidata":"https://www.wikidata.org/wiki/Q5422477","display_name":"Extremum estimator","level":4,"score":0.45554110407829285},{"id":"https://openalex.org/C194531419","wikidata":"https://www.wikidata.org/wiki/Q17104825","display_name":"Nuisance parameter","level":3,"score":0.4194268584251404},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41684186458587646},{"id":"https://openalex.org/C143791395","wikidata":"https://www.wikidata.org/wiki/Q475208","display_name":"M-estimator","level":3,"score":0.3867449164390564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.23088911175727844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12934499979019165}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:biostats.bepress.com:ucbbiostat-1305","doi":null,"is_oa":true,"landing_page_url":"https://biostats.bepress.com/ucbbiostat/paper302","pdf_url":"https://biostats.bepress.com/cgi/viewcontent.cgi?article=1305&context=ucbbiostat","source":{"id":"https://openalex.org/S4377196124","display_name":"Collection of Biostatistics Research Archive","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"U.C. Berkeley Division of Biostatistics Working Paper Series","raw_type":"text"},{"id":"mag:60695964","doi":null,"is_oa":false,"landing_page_url":"https://biostats.bepress.com/cgi/viewcontent.cgi?article=1305&context=ucbbiostat","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:biostats.bepress.com:ucbbiostat-1305","doi":null,"is_oa":true,"landing_page_url":"https://biostats.bepress.com/ucbbiostat/paper302","pdf_url":"https://biostats.bepress.com/cgi/viewcontent.cgi?article=1305&context=ucbbiostat","source":{"id":"https://openalex.org/S4377196124","display_name":"Collection of Biostatistics Research Archive","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"U.C. Berkeley Division of Biostatistics Working Paper Series","raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G5454551813","display_name":"Targeted Learning using adaptive designs for HIV Epidemic control in East Africa","funder_award_id":"R01AI074345","funder_id":"https://openalex.org/F4320337355","funder_display_name":"National Institute of Allergy and Infectious Diseases"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337355","display_name":"National Institute of Allergy and Infectious Diseases","ror":"https://ror.org/043z4tv69"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W60695964.pdf","grobid_xml":"https://content.openalex.org/works/W60695964.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1041910","https://openalex.org/W51467624","https://openalex.org/W67506904","https://openalex.org/W70543736","https://openalex.org/W128574488","https://openalex.org/W1572302037","https://openalex.org/W1926541588","https://openalex.org/W1967862917","https://openalex.org/W1977389736","https://openalex.org/W1987152354","https://openalex.org/W1987716761","https://openalex.org/W2008557562","https://openalex.org/W2014373672","https://openalex.org/W2018331991","https://openalex.org/W2022450888","https://openalex.org/W2032038468","https://openalex.org/W2032481853","https://openalex.org/W2051375321","https://openalex.org/W2078439407","https://openalex.org/W2080896439","https://openalex.org/W2101895213","https://openalex.org/W2107263285","https://openalex.org/W2117845059","https://openalex.org/W2136304036","https://openalex.org/W2146895648","https://openalex.org/W2168265006","https://openalex.org/W2168667982","https://openalex.org/W2214529849","https://openalex.org/W2269615221","https://openalex.org/W2796930163","https://openalex.org/W4248240383"],"related_works":["https://openalex.org/W2090978224","https://openalex.org/W7160235705","https://openalex.org/W2233016918","https://openalex.org/W2157445442","https://openalex.org/W2795929775","https://openalex.org/W2269615221","https://openalex.org/W7046362650","https://openalex.org/W2888161748","https://openalex.org/W2217990278","https://openalex.org/W2762307552"],"abstract_inverted_index":{"In":[0,93,229],"order":[1,94],"to":[2,95,112,187,222,305],"be":[3],"concrete":[4],"we":[5,231,285],"focus":[6],"on":[7,21,53,259],"estimation":[8,74,80],"of":[9,29,34,57,68,75,81,85,117,134,158,166,169,193,196,225,269,291,299],"the":[10,54,60,62,69,76,82,86,89,106,139,153,159,167,178,194,197,226,255,266,270,278,288,292,296,307],"treatment":[11,70,90,198,300],"specific":[12,71,199],"mean,":[13],"controlling":[14],"for":[15,152,177,244,254,277,295],"all":[16],"measured":[17],"baseline":[18,35],"covariates,":[19,36,61],"based":[20,258],"observing":[22],"n":[23],"independent":[24],"and":[25,42,91,125,136,183],"identically":[26],"distributed":[27],"copies":[28],"a":[30,37,43,148,260,281],"random":[31],"variable":[32,252],"consisting":[33],"subsequently":[38],"assigned":[39],"binary":[40],"treatment,":[41,58],"final":[44],"outcome.":[45],"The":[46],"statistical":[47,107,245],"model":[48],"only":[49],"assumes":[50],"possible":[51],"restrictions":[52],"conditional":[55,83],"distribution":[56,104],"given":[59,88],"so":[63],"called":[64],"propensity":[65,77,256,271,293,308],"score.":[66,309],"Estimators":[67],"mean":[72,84,200],"involve":[73,131,251],"score":[78,257,272,294],"and/or":[79],"outcome,":[87],"covariates.":[92],"make":[96],"these":[97,118,170,249],"estimators":[98,116,130,168,212,224,237],"asymptotically":[99],"unbiased":[100],"at":[101],"any":[102],"data":[103,114],"in":[105,147,207,274],"model,":[108],"it":[109],"is":[110,180,273],"essential":[111],"use":[113,215],"adaptive":[115],"nuisance":[119,142,171,227],"parameters":[120,172],"such":[121,129],"as":[122],"ensemble":[123,216],"learning,":[124],"specifically":[126],"super-learning.":[127],"Because":[128],"optimal":[132],"trade-off":[133,151],"bias":[135,176,220,276],"variance":[137],"w.r.t.":[138],"infinite":[140],"dimensional":[141],"parameter":[143],"itself,":[144],"they":[145],"result":[146,206],"sub-optimal":[149],"bias/variance":[150],"resulting":[154,267],"real":[155],"valued":[156],"estimator":[157,195,302],"estimand.":[160,279],"We":[161],"demonstrate":[162,287],"that":[163,174,214,262],"additional":[164],"targeting":[165,290],"guarantees":[173],"this":[175],"estimand":[179],"second":[181],"order,":[182],"thereby":[184],"allows":[185],"us":[186],"prove":[188],"theorems":[189],"thatestablish":[190],"asymptotic":[191],"linearity":[192],"under":[201],"regularity":[202],"conditions.":[203],"These":[204],"insights":[205],"novel":[208],"targeted":[209,219,234],"maximum":[210,235],"likelihood":[211,236],"(TMLE)":[213],"learning":[217],"withadditional":[218],"reduction":[221],"construct":[223,232],"parameters.":[228],"particular,":[230],"collaborative":[233],"(CTMLE)":[238],"with":[239],"known":[240],"influence":[241],"curve":[242],"allowing":[243],"inference,":[246],"even":[247],"though":[248],"CTMLEs":[250],"selection":[253],"criterion":[261],"measures":[263],"how":[264],"effective":[265],"fit":[268,306],"removing":[275],"As":[280],"particular":[282],"special":[283],"case,":[284],"also":[286],"required":[289],"inverse":[297],"probability":[298],"weighted":[301],"using":[303],"super-learning":[304]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}