Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes

Autores
de Mier, Manuel; Delbianco, Fernando Andrés; Tohmé, Fernando Abel; Patrizio, Luisina; Rodríguez, Facundo; Romero Stefani, Mauro
Año de publicación
2023
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper we investigate the performance of five causality-detection methods and how their results can be aggregated when multiple units are considered in a panel data setting. The aggregation procedure employs voting rules for determining which causal paths are identified for the sample population. Using simulated and real-world panel data, we show the performance of these methods in detecting the correct causal paths in comparison to a benchmark that comprises a standard representation of growth processes as ground truth model. We find that the results may be better when only simulated, instead of real-world, data are analyzed. While this may suggest that the methods presented here are currently incapable of detecting causal links, it is plausible that the ground ``truth'' may incorporate false relations.
Fil: de Mier, Manuel. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Patrizio, Luisina. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Rodríguez, Facundo. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Romero Stefani, Mauro. Universidad Nacional del Sur. Departamento de Economía; Argentina
LVIII Reunión Anual de la Asociación Argentina de Economía Política
Mendoza
Argentina
Asociación Argentina de Economía Política
Materia
GRANGER CAUSALITY
TRANSFER ENTROPY
STOCHASTIC CAUSALITY
LiNGAM
GROUND TRUTH
ECONOMIC GROWTH
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/238345
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processesde Mier, ManuelDelbianco, Fernando AndrésTohmé, Fernando AbelPatrizio, LuisinaRodríguez, FacundoRomero Stefani, MauroGRANGER CAUSALITYTRANSFER ENTROPYSTOCHASTIC CAUSALITYLiNGAMGROUND TRUTHECONOMIC GROWTHhttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5In this paper we investigate the performance of five causality-detection methods and how their results can be aggregated when multiple units are considered in a panel data setting. The aggregation procedure employs voting rules for determining which causal paths are identified for the sample population. Using simulated and real-world panel data, we show the performance of these methods in detecting the correct causal paths in comparison to a benchmark that comprises a standard representation of growth processes as ground truth model. We find that the results may be better when only simulated, instead of real-world, data are analyzed. While this may suggest that the methods presented here are currently incapable of detecting causal links, it is plausible that the ground ``truth'' may incorporate false relations.Fil: de Mier, Manuel. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Patrizio, Luisina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Rodríguez, Facundo. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Romero Stefani, Mauro. Universidad Nacional del Sur. Departamento de Economía; ArgentinaLVIII Reunión Anual de la Asociación Argentina de Economía PolíticaMendozaArgentinaAsociación Argentina de Economía PolíticaAsociación Argentina de Economía Política2023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/238345Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes; LVIII Reunión Anual de la Asociación Argentina de Economía Política; Mendoza; Argentina; 2023; 1-19CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://aaep.org.ar/?p=7124Nacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:06:00Zoai:ri.conicet.gov.ar:11336/238345instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:06:00.423CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
title Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
spellingShingle Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
de Mier, Manuel
GRANGER CAUSALITY
TRANSFER ENTROPY
STOCHASTIC CAUSALITY
LiNGAM
GROUND TRUTH
ECONOMIC GROWTH
title_short Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
title_full Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
title_fullStr Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
title_full_unstemmed Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
title_sort Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
dc.creator.none.fl_str_mv de Mier, Manuel
Delbianco, Fernando Andrés
Tohmé, Fernando Abel
Patrizio, Luisina
Rodríguez, Facundo
Romero Stefani, Mauro
author de Mier, Manuel
author_facet de Mier, Manuel
Delbianco, Fernando Andrés
Tohmé, Fernando Abel
Patrizio, Luisina
Rodríguez, Facundo
Romero Stefani, Mauro
author_role author
author2 Delbianco, Fernando Andrés
Tohmé, Fernando Abel
Patrizio, Luisina
Rodríguez, Facundo
Romero Stefani, Mauro
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv GRANGER CAUSALITY
TRANSFER ENTROPY
STOCHASTIC CAUSALITY
LiNGAM
GROUND TRUTH
ECONOMIC GROWTH
topic GRANGER CAUSALITY
TRANSFER ENTROPY
STOCHASTIC CAUSALITY
LiNGAM
GROUND TRUTH
ECONOMIC GROWTH
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv In this paper we investigate the performance of five causality-detection methods and how their results can be aggregated when multiple units are considered in a panel data setting. The aggregation procedure employs voting rules for determining which causal paths are identified for the sample population. Using simulated and real-world panel data, we show the performance of these methods in detecting the correct causal paths in comparison to a benchmark that comprises a standard representation of growth processes as ground truth model. We find that the results may be better when only simulated, instead of real-world, data are analyzed. While this may suggest that the methods presented here are currently incapable of detecting causal links, it is plausible that the ground ``truth'' may incorporate false relations.
Fil: de Mier, Manuel. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Patrizio, Luisina. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Rodríguez, Facundo. Universidad Nacional del Sur. Departamento de Economía; Argentina
Fil: Romero Stefani, Mauro. Universidad Nacional del Sur. Departamento de Economía; Argentina
LVIII Reunión Anual de la Asociación Argentina de Economía Política
Mendoza
Argentina
Asociación Argentina de Economía Política
description In this paper we investigate the performance of five causality-detection methods and how their results can be aggregated when multiple units are considered in a panel data setting. The aggregation procedure employs voting rules for determining which causal paths are identified for the sample population. Using simulated and real-world panel data, we show the performance of these methods in detecting the correct causal paths in comparison to a benchmark that comprises a standard representation of growth processes as ground truth model. We find that the results may be better when only simulated, instead of real-world, data are analyzed. While this may suggest that the methods presented here are currently incapable of detecting causal links, it is plausible that the ground ``truth'' may incorporate false relations.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Book
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/238345
Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes; LVIII Reunión Anual de la Asociación Argentina de Economía Política; Mendoza; Argentina; 2023; 1-19
CONICET Digital
CONICET
url http://hdl.handle.net/11336/238345
identifier_str_mv Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes; LVIII Reunión Anual de la Asociación Argentina de Economía Política; Mendoza; Argentina; 2023; 1-19
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://aaep.org.ar/?p=7124
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv Asociación Argentina de Economía Política
publisher.none.fl_str_mv Asociación Argentina de Economía Política
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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