{"id":"https://openalex.org/W104554486","doi":null,"title":"Semi-parametric graphical computation approach using loss-based estimation to estimate exposure effects: applications on infant developmental outcomes","display_name":"Semi-parametric graphical computation approach using loss-based estimation to estimate exposure effects: applications on infant developmental outcomes","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W104554486","mag":"104554486"},"language":"en","primary_location":{"id":"mag:104554486","doi":null,"is_oa":false,"landing_page_url":"https://escholarship.org/content/qt1z4447wr/qt1z4447wr.pdf?t=mtfeik","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},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037685846","display_name":"Aguilar Schall","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aguilar Schall","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058050102","display_name":"Ra\u00fal Eduardo","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raul Eduardo","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01288391,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9868000149726868,"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":0.9868000149726868,"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":0.007000000216066837,"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":0.0010000000474974513,"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":0.9559999704360962},{"id":"https://openalex.org/keywords/semiparametric-estimation","display_name":"semiparametric estimation","score":0.7540000081062317},{"id":"https://openalex.org/keywords/g-computation","display_name":"g-computation","score":0.7070000171661377},{"id":"https://openalex.org/keywords/intermediate-variables","display_name":"intermediate variables","score":0.7630000114440918},{"id":"https://openalex.org/keywords/maternal-depression","display_name":"maternal depression","score":0.6830000281333923},{"id":"https://openalex.org/keywords/children-s-cognitive-development","display_name":"child cognitive development","score":0.7179999947547913},{"id":"https://openalex.org/keywords/child-language-acquisition","display_name":"child language acquisition","score":0.902999997138977},{"id":"https://openalex.org/keywords/organophosphate-pesticides","display_name":"organophosphorus pesticides","score":0.8730000257492065},{"id":"https://openalex.org/keywords/prenatal-pesticide-exposure","display_name":"prenatal pesticide exposure","score":0.8820000290870667},{"id":"https://openalex.org/keywords/child-growth","display_name":"child growth","score":0.8320000171661377},{"id":"https://openalex.org/keywords/salinas-valley","display_name":"Salinas Valley","score":0.6380000114440918},{"id":"https://openalex.org/keywords/intervention-effect-estimation","display_name":"intervention effect estimation","score":0.5720000267028809},{"id":"https://openalex.org/keywords/causal-inference","display_name":"causal inference","score":0.40400001406669617},{"id":"https://openalex.org/keywords/observational-studies","display_name":"observational studies","score":0.5590000152587891},{"id":"https://openalex.org/keywords/confounding","display_name":"confounding","score":0.6140000224113464},{"id":"https://openalex.org/keywords/super-learner","display_name":"super learner","score":0.8379999995231628}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5981851816177368},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5853511691093445},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.5489332675933838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4880439341068268},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.48456302285194397},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.4646875858306885},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4516727328300476},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.45151615142822266},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.41560930013656616},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40128815174102783},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33544886112213135},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2915874719619751},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:104554486","doi":null,"is_oa":false,"landing_page_url":"https://escholarship.org/content/qt1z4447wr/qt1z4447wr.pdf?t=mtfeik","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":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.9391000270843506}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W6178129","https://openalex.org/W146111145","https://openalex.org/W560298130","https://openalex.org/W1501375624","https://openalex.org/W1512173565","https://openalex.org/W1542038388","https://openalex.org/W1546721342","https://openalex.org/W1550957955","https://openalex.org/W1804627766","https://openalex.org/W1963901635","https://openalex.org/W1969115978","https://openalex.org/W1971440513","https://openalex.org/W1973285887","https://openalex.org/W1984581064","https://openalex.org/W1996377580","https://openalex.org/W2004180568","https://openalex.org/W2005078229","https://openalex.org/W2006825115","https://openalex.org/W2009187570","https://openalex.org/W2012658469","https://openalex.org/W2014596926","https://openalex.org/W2016709601","https://openalex.org/W2016769893","https://openalex.org/W2022450888","https://openalex.org/W2035142205","https://openalex.org/W2036323892","https://openalex.org/W2063978378","https://openalex.org/W2064790857","https://openalex.org/W2066210102","https://openalex.org/W2069157573","https://openalex.org/W2076652804","https://openalex.org/W2082299845","https://openalex.org/W2092961001","https://openalex.org/W2093174458","https://openalex.org/W2102201073","https://openalex.org/W2112778345","https://openalex.org/W2121611663","https://openalex.org/W2133774931","https://openalex.org/W2135051310","https://openalex.org/W2141130068","https://openalex.org/W2148429341","https://openalex.org/W2152255737","https://openalex.org/W2155160079","https://openalex.org/W2156155314","https://openalex.org/W2160610058","https://openalex.org/W2163935588","https://openalex.org/W2166163519","https://openalex.org/W2170529711","https://openalex.org/W2314734263","https://openalex.org/W2331384579","https://openalex.org/W2767093715"],"related_works":["https://openalex.org/W1603133327","https://openalex.org/W2560774707","https://openalex.org/W206071239","https://openalex.org/W1488660595","https://openalex.org/W2098818418","https://openalex.org/W2756475937","https://openalex.org/W7006375807","https://openalex.org/W130514121","https://openalex.org/W7202381995","https://openalex.org/W2463468932"],"abstract_inverted_index":{"In":[0],"epidemiology,":[1],"researchers":[2],"try":[3],"to":[4,38,97,184,195,217,280,288,348,401,408,440],"answer":[5],"questions":[6],"about":[7,381],"exposures":[8],"and":[9,80,137,199,227,257,305,320,370,438,445,523],"their":[10],"effects":[11,167,202,259,285,344,384,404,430],"(associations)":[12],"on":[13,57,78,133,193,225,260,351,389,476,486,500],"a":[14,139,274,398,424,493,517,524],"variety":[15],"of":[16,18,66,69,89,102,105,118,124,127,141,159,177,222,241,283,286,345,359,366,385,405,418,431,461,496,510,519,526],"outcomes":[17],"interest.":[19,128],"Most":[20],"times,":[21],"the":[22,31,36,87,99,103,119,125,142,146,151,157,164,175,181,188,197,207,219,235,258,264,284,300,328,333,346,356,360,364,382,403,415,419,429,441,447,455,477,487,501,520],"collected":[23],"data":[24],"comes":[25],"from":[26,86,451],"observational":[27],"studies,":[28],"meaning":[29],"that":[30],"researcher":[32],"did":[33],"not":[34,76,291],"control":[35,425],"exposure":[37,256,287,347,407,435,499],"which":[39,60,114],"each":[40],"subject":[41],"under":[42,72,335],"study":[43,336],"was":[44,436],"exposed,":[45],"like":[46],"it":[47],"is":[48,138,148,172,279,302,397],"done":[49],"in":[50,230,234,252,294,327,411,414,481],"clinical":[51],"trials.":[52],"Additionally,":[53],"researches":[54],"collect":[55],"information":[56],"other":[58],"variables":[59,209],"could":[61],"act":[62],"as":[63,372,374],"potential":[64],"confounders":[65],"exposure.":[67],"Estimation":[68],"adjusted":[70],"associations":[71],"these":[73],"conditions,":[74],"if":[75],"reliant":[77],"arbitrary":[79],"thus":[81],"biased":[82],"parametric":[83],"models,":[84],"suffers":[85],"curse":[88],"dimensionality.":[90],"This":[91,396],"dissertation":[92],"describes":[93],"semi-parametric":[94],"statistical":[95],"approaches":[96],"address":[98],"correct":[100],"estimation":[101,111,123,158],"parameter":[104,147],"interest":[106],"using":[107,180,203],"targeted":[108],"maximum":[109],"likelihood":[110],"(TMLE)":[112],"methodology,":[113],"optimally":[115,131],"adapts":[116],"estimates":[117,282],"data-generating":[120],"distribution":[121,479],"for":[122,163,187,206],"association":[126],"The":[129,277],"process":[130],"relies":[132],"machine":[134,367],"learning":[135,368],"techniques":[136,213,369],"modification":[140],"likelihood-based":[143],"algorithm":[144],"where":[145,332,434],"defined":[149],"by":[150,393],"so-called":[152],"G-computation":[153,488],"formula.Chapter":[154],"2":[155],"provides":[156],"direct":[160,189,201,220],"effects,":[161],"adjusting":[162],"possible":[165],"indirect":[166],"through":[168,363],"intermediate":[169,208],"variables.":[170],"TMLE":[171,204,275],"used,":[173],"with":[174,243,454,483,516],"help":[176],"model":[178],"selection":[179],"SuperLearner":[182],"algorithm,":[183],"obtain":[185,281],"estimators":[186],"effect.":[190],"General":[191],"methods":[192],"how":[194,306],"estimate":[196,402,480],"natural":[198],"controlled":[200,437],"controlling":[205],"are":[210,214,324,377],"implemented.":[211],"These":[212],"then":[215],"used":[216,268,326,464],"examine":[218],"effect":[221,362,495],"maternal":[223],"depression":[224],"cognitive":[226],"language":[228],"development":[229],"350":[231],"Mexican-American":[232],"children":[233,412],"CHAMACOS":[236],"birth":[237],"cohort":[238],"study.":[239],"Children":[240],"mothers":[242],"depressive":[244],"symptoms":[245],"scored":[246],"significantly":[247],"lower":[248],"(-2:82":[249],"(p-value":[250],"utero":[253,295],"pesticide":[255,498],"fetal":[261],"growth.":[262],"All":[263],"previous":[265],"literature":[266],"has":[267],"traditional":[269],"analyses,":[270],"while":[271],"we":[272,427],"implement":[273],"approach.":[276],"goal":[278],"OP":[289,349,409,497],"pesticides":[290,350,388,410],"only":[292],"but":[297,470],"later,":[298],"when":[299],"child":[301,390,394,449,503],"exposed":[303],"directly":[304],"this":[307,452],"affects":[308],"its":[309],"physical":[310],"growth":[311,391],"at":[312,458,505],"different":[313],"ages:":[314],"6,":[315],"12,":[316],"24":[317],"months,":[318],"3.5,":[319],"5":[321,514],"years.":[322],"Pesticides":[323],"widely":[325],"Salinas":[329,420],"Valley,":[330,421],"CA":[331],"population":[334],"resides.":[337],"We":[338,463],"identify":[339],"several":[340],"statistically":[341],"significant":[342],"negative":[343,494],"child's":[352],"growth.Chapter":[353],"4":[354],"presents":[355],"longitudinal":[357,383],"analysis":[358],"intervention":[361,433],"use":[365],"G-computation,":[371],"well":[373],"TMLE.":[375],"There":[376],"no":[378],"available":[379],"studies":[380],"organophosphate":[386],"(OP)":[387],"measured":[392],"weight.":[395],"first":[399],"attempt":[400],"continuous":[406],"living":[413],"agricultural":[416],"region":[417],"CA.":[422],"Without":[423],"group,":[426],"estimated":[428,448],"an":[432,465],"fixed":[439],"lowest":[442],"level":[443],"possible,":[444],"compared":[446],"weights":[450,457],"scenario":[453],"actual":[456],"3.5":[459,506],"years":[460],"age.":[462],"ad":[467],"hoc":[468],",":[469],"still":[471],"double":[472],"robust,":[473],"targeting":[474],"step":[475],"outcome":[478],"conjunction":[482],"simulation":[484],"based":[485],"formula.":[489],"Our":[490],"results":[491],"show":[492],"mean":[502],"weight":[504],"years,":[507],"however":[508],"none":[509],"them":[511],"reached":[512],"significance.Chapter":[513],"concludes":[515],"summary":[518],"preceding":[521],"chapters":[522],"discussion":[525],"future":[527],"research":[528],"directions.":[529]},"counts_by_year":[],"updated_date":"2025-11-06T04:12:42.849631","created_date":"2016-06-24T00:00:00"}
