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Related Concept Videos

Correlation and Causation01:27

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...
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:
Causality in Epidemiology01:21

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...
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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:
Cause and Effect01:53

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?
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

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Related Experiment Video

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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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Published on: August 7, 2017

The effect of filtering on Granger causality based multivariate causality measures.

Esther Florin1, Joachim Gross, Johannes Pfeifer

  • 1Department of Neurology, University Hospital Cologne, Cologne, Germany. e.florin@fz.juelich.de

Neuroimage
|December 23, 2009
PubMed
Summary

Preprocessing neural data with filters can distort causality measures, leading to inaccurate findings. Careful consideration of filtering techniques is crucial for reliable neural signal analysis.

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Computational Biology

Background:

  • Granger causality is used for neural signal directionality.
  • Frequency domain analyses often involve filtering or decimating neural time series.
  • Prior research suggests filtering can cause spurious or missed causalities.

Purpose of the Study:

  • Investigate if filtering affects multivariate causality measures derived from Granger causality.
  • Evaluate the impact of preprocessing on neural data causality analysis.
  • Assess the reliability of causality measures under different preprocessing conditions.

Main Methods:

  • Conducted extensive simulations using various filtering techniques (high-pass, low-pass, notch) and filter types (Butterworth, Chebyshev, elliptic).
  • Applied five multivariate causality measures and two significance measures (random permutation, leave one out) to simulated and magnetoencephalographic data.
  • Varied filter order, decimation, and interpolation parameters during simulations.

Main Results:

  • Data preprocessing, especially filtering without a clear artifact removal goal, introduces spurious and missed causalities.
  • Filtering is advisable only for removing specific artifacts (e.g., movement artifacts).
  • Decimation by a factor larger than the minimum time shift can lead to incorrect inferences; oversampling is generally not problematic.

Conclusions:

  • Multivariate causality measures are highly sensitive to data preprocessing.
  • Standard filtering and decimation techniques can significantly compromise the accuracy of neural causality assessments.
  • Researchers must carefully evaluate preprocessing steps to ensure the validity of Granger causality-based findings in neural data analysis.