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Criteria for Causality: Bradford Hill Criteria - II

Criteria for Causality: Bradford Hill Criteria - II

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:

Criteria for Causality: Bradford Hill Criteria - I

Criteria for Causality: Bradford Hill Criteria - I

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:

Causality in Epidemiology

Causality in Epidemiology

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...

Correlation and Causation

Correlation and Causation

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...

Cause and Effect

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?

Entropy Change in Reversible Processes

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.

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Related Experiment Video

Updated: Apr 27, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Granger causality revisited.

Karl J Friston¹, André M Bastos², Ashwini Oswal¹

¹The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.

|July 9, 2014

Summary

This summary is machine-generated.

This study re-evaluates spectral Granger causality in biological time-series analysis. Nonparametric methods are robust to dynamical instability and noise, especially when using Volterra kernels from dynamic causal modeling.

Keywords:

Cross spectra Dynamic causal modelling Dynamics Effective connectivity Functional connectivity Granger causality Neurophysiology

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Published on: April 18, 2017

Area of Science:

Neuroscience
Computational Biology
Time Series Analysis

Background:

Spectral Granger causality is widely used for analyzing biological time-series data.
Assessing the reliability of these measures under different dynamic conditions is crucial.

Purpose of the Study:

To critically re-evaluate parametric and nonparametric spectral Granger causality measures.
To investigate their robustness against dynamical instability and measurement noise in neuronal models.

Main Methods:

Utilized realistic neural mass models (state-space models) of coupled neuronal dynamics.
Analyzed second-order statistics (cross-spectral density, cross-covariance, autoregressive coefficients, directed transfer functions).
Linked generative model parameters to expected Granger causality via Volterra kernels.

Main Results:

Autoregressive-based Granger causality measures are unreliable with slow (unstable) dynamics (high Lyapunov exponent).
Nonparametric measures using causal spectral factors demonstrate robustness to dynamical instability.
Both parametric and nonparametric measures become unreliable with measurement noise.

Conclusions:

Nonparametric spectral causality measures derived from Volterra kernels (estimated via dynamic causal modeling) offer a robust solution against dynamical instability and measurement noise.
This approach enhances the reliability of Granger causality analysis in neuroscience.
Highlights the importance of model-based causality assessment in complex biological systems.