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
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