What is a relevant control?: an algorithmic proposal

Autores
Delbianco, Fernando; Tohmé, Fernando Abel
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Individualized inference (or prediction) is an approach to data analysis that is increasingly relevant thanks to the availability of large datasets. In this paper, we present an algorithm that starts by detecting the relevant observations for a given query. Further refinement of that subsample is obtained by selecting the ones with the largest Shapley values. The probability distribution over this selection allows to generate synthetic controls, which in turn can be used to generate a robust inference (or prediction). Data collected from repeating this procedure for different queries provides a deeper understanding of the general process that generates the data.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Individualized inference
Relevance selection
Relevance classification
Synthetic controls
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/177170
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/177170
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling What is a relevant control?: an algorithmic proposalDelbianco, FernandoTohmé, Fernando AbelCiencias InformáticasIndividualized inferenceRelevance selectionRelevance classificationSynthetic controlsIndividualized inference (or prediction) is an approach to data analysis that is increasingly relevant thanks to the availability of large datasets. In this paper, we present an algorithm that starts by detecting the relevant observations for a given query. Further refinement of that subsample is obtained by selecting the ones with the largest Shapley values. The probability distribution over this selection allows to generate synthetic controls, which in turn can be used to generate a robust inference (or prediction). Data collected from repeating this procedure for different queries provides a deeper understanding of the general process that generates the data.Sociedad Argentina de Informática e Investigación Operativa2024-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf15-27http://sedici.unlp.edu.ar/handle/10915/177170enginfo:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/17925info:eu-repo/semantics/altIdentifier/issn/2451-7496info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:19:41Zoai:sedici.unlp.edu.ar:10915/177170Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:19:41.853SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv What is a relevant control?: an algorithmic proposal
title What is a relevant control?: an algorithmic proposal
spellingShingle What is a relevant control?: an algorithmic proposal
Delbianco, Fernando
Ciencias Informáticas
Individualized inference
Relevance selection
Relevance classification
Synthetic controls
title_short What is a relevant control?: an algorithmic proposal
title_full What is a relevant control?: an algorithmic proposal
title_fullStr What is a relevant control?: an algorithmic proposal
title_full_unstemmed What is a relevant control?: an algorithmic proposal
title_sort What is a relevant control?: an algorithmic proposal
dc.creator.none.fl_str_mv Delbianco, Fernando
Tohmé, Fernando Abel
author Delbianco, Fernando
author_facet Delbianco, Fernando
Tohmé, Fernando Abel
author_role author
author2 Tohmé, Fernando Abel
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Individualized inference
Relevance selection
Relevance classification
Synthetic controls
topic Ciencias Informáticas
Individualized inference
Relevance selection
Relevance classification
Synthetic controls
dc.description.none.fl_txt_mv Individualized inference (or prediction) is an approach to data analysis that is increasingly relevant thanks to the availability of large datasets. In this paper, we present an algorithm that starts by detecting the relevant observations for a given query. Further refinement of that subsample is obtained by selecting the ones with the largest Shapley values. The probability distribution over this selection allows to generate synthetic controls, which in turn can be used to generate a robust inference (or prediction). Data collected from repeating this procedure for different queries provides a deeper understanding of the general process that generates the data.
Sociedad Argentina de Informática e Investigación Operativa
description Individualized inference (or prediction) is an approach to data analysis that is increasingly relevant thanks to the availability of large datasets. In this paper, we present an algorithm that starts by detecting the relevant observations for a given query. Further refinement of that subsample is obtained by selecting the ones with the largest Shapley values. The probability distribution over this selection allows to generate synthetic controls, which in turn can be used to generate a robust inference (or prediction). Data collected from repeating this procedure for different queries provides a deeper understanding of the general process that generates the data.
publishDate 2024
dc.date.none.fl_str_mv 2024-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/177170
url http://sedici.unlp.edu.ar/handle/10915/177170
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/17925
info:eu-repo/semantics/altIdentifier/issn/2451-7496
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
15-27
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.13397

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