Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach

Abstract. Causal inference has become a dominant research area in both theoretical and empirical statistics. One of the main drawbacks of conventional frameworks is the assumption of no causal interactions among individuals (i.e independent units). Violation of this assumption often yields biased es...

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Autores:
Cárdenas Hurtado, Camilo Alberto
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/59495
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/59495
http://bdigital.unal.edu.co/57011/
Palabra clave:
31 Colecciones de estadística general / Statistics
51 Matemáticas / Mathematics
Causal inference
Bayesian estimation
Independence assumption violation
Causally connected units
Directed acyciclic graphs (DAG)
Structural equation models
Hierarchical linear models
Semiparametric models
Inferencia causal
Violación de supuesto de independencia
Dependencia entre observaciones
Grafos acíclicos direccionados (DAG)
Modelos de ecuaciones estructurales (SEM)
Modelos jerárquicos (HLM)
Modelos semiparamétricos
e Estimación Bayesiana
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_aa0708cc0fd22219bee19e7fc6b7cc48
oai_identifier_str oai:repositorio.unal.edu.co:unal/59495
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach
title Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach
spellingShingle Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach
31 Colecciones de estadística general / Statistics
51 Matemáticas / Mathematics
Causal inference
Bayesian estimation
Independence assumption violation
Causally connected units
Directed acyciclic graphs (DAG)
Structural equation models
Hierarchical linear models
Semiparametric models
Inferencia causal
Violación de supuesto de independencia
Dependencia entre observaciones
Grafos acíclicos direccionados (DAG)
Modelos de ecuaciones estructurales (SEM)
Modelos jerárquicos (HLM)
Modelos semiparamétricos
e Estimación Bayesiana
title_short Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach
title_full Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach
title_fullStr Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach
title_full_unstemmed Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach
title_sort Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach
dc.creator.fl_str_mv Cárdenas Hurtado, Camilo Alberto
dc.contributor.author.spa.fl_str_mv Cárdenas Hurtado, Camilo Alberto
dc.contributor.spa.fl_str_mv Urdinola Contreras, B. Piedad
dc.subject.ddc.spa.fl_str_mv 31 Colecciones de estadística general / Statistics
51 Matemáticas / Mathematics
topic 31 Colecciones de estadística general / Statistics
51 Matemáticas / Mathematics
Causal inference
Bayesian estimation
Independence assumption violation
Causally connected units
Directed acyciclic graphs (DAG)
Structural equation models
Hierarchical linear models
Semiparametric models
Inferencia causal
Violación de supuesto de independencia
Dependencia entre observaciones
Grafos acíclicos direccionados (DAG)
Modelos de ecuaciones estructurales (SEM)
Modelos jerárquicos (HLM)
Modelos semiparamétricos
e Estimación Bayesiana
dc.subject.proposal.spa.fl_str_mv Causal inference
Bayesian estimation
Independence assumption violation
Causally connected units
Directed acyciclic graphs (DAG)
Structural equation models
Hierarchical linear models
Semiparametric models
Inferencia causal
Violación de supuesto de independencia
Dependencia entre observaciones
Grafos acíclicos direccionados (DAG)
Modelos de ecuaciones estructurales (SEM)
Modelos jerárquicos (HLM)
Modelos semiparamétricos
e Estimación Bayesiana
description Abstract. Causal inference has become a dominant research area in both theoretical and empirical statistics. One of the main drawbacks of conventional frameworks is the assumption of no causal interactions among individuals (i.e independent units). Violation of this assumption often yields biased estimations of causal effects of an intervention in quantitative social, biomedical and epidemiological research. This document proposes a novel approach for modeling causal connections among units within the Structural Causal Model framework: a Semi-Parametric Hierarchical Structural Equation Model (SPHSEM). Estimation uses Bayesian techniques, and the empirical performance of the proposed model is evaluated through both simulation and applied studies. Results prove that the Bayesian SPHSEM recovers nonlinear (causal) relationships between latent variables belonging to different levels and yields unbiased estimates of the (causal) model parameters.
publishDate 2017
dc.date.issued.spa.fl_str_mv 2017
dc.date.accessioned.spa.fl_str_mv 2019-07-02T16:11:47Z
dc.date.available.spa.fl_str_mv 2019-07-02T16:11:47Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/59495
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/57011/
url https://repositorio.unal.edu.co/handle/unal/59495
http://bdigital.unal.edu.co/57011/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Sede Bogotá Facultad de Ciencias Departamento de Estadística
Departamento de Estadística
dc.relation.references.spa.fl_str_mv Cárdenas Hurtado, Camilo Alberto (2017) Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach. Maestría thesis, Universidad Nacional de Colombia, Sede Bogotá.
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/59495/1/CamiloC%c3%a1rdenasHurtado.2016.pdf
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repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Urdinola Contreras, B. PiedadCárdenas Hurtado, Camilo Albertod82b39c4-8fa8-40aa-b762-d841b60a6f8f3002019-07-02T16:11:47Z2019-07-02T16:11:47Z2017https://repositorio.unal.edu.co/handle/unal/59495http://bdigital.unal.edu.co/57011/Abstract. Causal inference has become a dominant research area in both theoretical and empirical statistics. One of the main drawbacks of conventional frameworks is the assumption of no causal interactions among individuals (i.e independent units). Violation of this assumption often yields biased estimations of causal effects of an intervention in quantitative social, biomedical and epidemiological research. This document proposes a novel approach for modeling causal connections among units within the Structural Causal Model framework: a Semi-Parametric Hierarchical Structural Equation Model (SPHSEM). Estimation uses Bayesian techniques, and the empirical performance of the proposed model is evaluated through both simulation and applied studies. Results prove that the Bayesian SPHSEM recovers nonlinear (causal) relationships between latent variables belonging to different levels and yields unbiased estimates of the (causal) model parameters.La inferencia causal se ha convertido en un área activa de investigación en la estadística teórica y aplicada. Una falencia de las aproximaciones convencionales es el supuesto de ausencia de interacciones causales entre individuos (unidades independientes de estudio). La violación de este supuesto resulta en estimaciones sesgadas de los efectos causales en investigaciones sociales, biomédicas y epidemiológicas. En este documento se propone una nueva manera de modelar dichas conexiones causales bajo el Modelo Estructural de Causalidad: un modelo Semi-Paramétrico, Jerárquico de Ecuaciones Estructurales (SPHSEM). La estimación se hace mediante técnicas Bayesianas, y su capacidad empírica se evalúa a través tanto de un ejercicio de simulación como de una aplicación empírica. Los resultados confirman que el SPHSEM Bayesiana recupera las relaciones causales no lineales que existen entre variables latentes pertenecientes a distintos niveles de agrupamiento, y que las estimaciones de los parámetros causales son insesgadas.Maestríaapplication/pdfspaUniversidad Nacional de Colombia Sede Bogotá Facultad de Ciencias Departamento de EstadísticaDepartamento de EstadísticaCárdenas Hurtado, Camilo Alberto (2017) Causal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approach. Maestría thesis, Universidad Nacional de Colombia, Sede Bogotá.31 Colecciones de estadística general / Statistics51 Matemáticas / MathematicsCausal inferenceBayesian estimationIndependence assumption violationCausally connected unitsDirected acyciclic graphs (DAG)Structural equation modelsHierarchical linear modelsSemiparametric modelsInferencia causalViolación de supuesto de independenciaDependencia entre observacionesGrafos acíclicos direccionados (DAG)Modelos de ecuaciones estructurales (SEM)Modelos jerárquicos (HLM)Modelos semiparamétricose Estimación BayesianaCausal Inference in the presence of causally connected units: a semi-parametric hierarchical structural equation model approachTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMORIGINALCamiloCárdenasHurtado.2016.pdfapplication/pdf3340962https://repositorio.unal.edu.co/bitstream/unal/59495/1/CamiloC%c3%a1rdenasHurtado.2016.pdf9ef6935252773dea0d89f487d35f5281MD51THUMBNAILCamiloCárdenasHurtado.2016.pdf.jpgCamiloCárdenasHurtado.2016.pdf.jpgGenerated Thumbnailimage/jpeg4225https://repositorio.unal.edu.co/bitstream/unal/59495/2/CamiloC%c3%a1rdenasHurtado.2016.pdf.jpgbcaa163e94b238fb823ef136e3799518MD52unal/59495oai:repositorio.unal.edu.co:unal/594952023-04-02 23:05:14.68Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co

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