Conditional variational autoencoders for causal inference with mismeasured treatments

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorPöllänen, Antti
dc.contributor.authorLe, Son
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorKäpylä, Maarit
dc.date.accessioned2022-05-24T08:10:27Z
dc.date.available2022-05-24T08:10:27Z
dc.date.issued2022-04-15
dc.format.extent44+1
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/114573
dc.identifier.urnURN:NBN:fi:aalto-202205243420
dc.language.isoenen
dc.programmeAalto Bachelor’s Programme in Science and Technologyfi
dc.programme.majorData Scienceen
dc.programme.mcodeSCI3095fi
dc.subject.keywordconditional variational autoencoderen
dc.subject.keywordimportance samplingen
dc.subject.keywordmeasurement erroren
dc.subject.keywordcausal inferenceen
dc.subject.keywordaverage dose-response functionen
dc.titleConditional variational autoencoders for causal inference with mismeasured treatmentsen
dc.typeG1 Kandidaatintyöfi
dc.type.dcmitypetexten
dc.type.ontasotBachelor's thesisen
dc.type.ontasotKandidaatintyöfi

Files