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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Criteria for Causality: Bradford Hill Criteria - II01:28

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Causalidad de Granger jacobiana para datos de recuento y binarios con aplicaciones a la inferencia de redes causales

Suryadi1, Lock Yue Chew2, Yew-Soon Ong3

  • 1School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore, Singapore.

Scientific reports
|December 21, 2025
PubMed
Resumen

Este estudio extiende la causalidad de Granger basada en redes neuronales para datos neuronales discretos. El método mejorado infiere con precisión redes neuronales a partir de datos dispersos de recuento y binarios, revelando información sobre el procesamiento visual.

Palabras clave:
Causalidad de GrangerAprendizaje automáticoSeries temporales

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Área de la Ciencia:

  • Neurociencia
  • Neurociencia Computacional
  • Aprendizaje Automático

Sus antecedentes:

  • La causalidad de Granger es vital para la inferencia de redes neuronales.
  • Las formulaciones actuales de redes neuronales artificiales para la causalidad de Granger son excelentes con datos continuos, pero tienen dificultades con datos neuronales discretos y dispersos.
  • Existen limitaciones en la aplicación de los métodos existentes a la actividad neuronal de recuento y binaria.

Objetivo del estudio:

  • Adaptar la causalidad de Granger jacobiana para tipos de datos neuronales discretos (recuento y binarios).
  • Abordar las limitaciones de las formulaciones optimizadas para datos continuos en sistemas neuronales discretos y dispersos.
  • Evaluar el rendimiento del método extendido frente a los enfoques existentes.

Principales métodos:

  • Causalidad de Granger jacobiana extendida utilizando funciones de pérdida especializadas para datos de recuento y binarios.
  • Se utilizaron conjuntos de datos simulados para comparar el novedoso enfoque con un método de la competencia.
  • Se aplicó el método de causalidad de Granger mejorado a datos reales de potenciales de acción neuronales de la corteza visual de monos.

Principales resultados:

  • El método adaptado de causalidad de Granger jacobiana demuestra efectividad con datos neuronales discretos.
  • Las simulaciones confirmaron el rendimiento del método frente a un enfoque de la competencia.
  • El análisis de datos de la corteza visual de monos reveló actividad neuronal estructurada bajo estímulos de películas naturales, incluidas mayores auto-conexiones positivas en las neuronas.

Conclusiones:

  • La causalidad de Granger jacobiana extendida proporciona un marco robusto para inferir redes neuronales a partir de datos neuronales discretos y dispersos.
  • Los estímulos de películas naturales inducen una actividad neuronal más estructurada en comparación con el ruido blanco.
  • Las auto-conexiones positivas en las neuronas, que potencialmente codifican información visual saliente, son más prevalentes durante el procesamiento visual naturalista.