Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

1.3K
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...
1.3K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

948
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:
948
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

795
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:
795
Correlation and Causation01:27

Correlation and Causation

40.4K
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...
40.4K
Cause and Effect01:53

Cause and Effect

11.8K
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?
11.8K
Gene-Environment Interactions01:20

Gene-Environment Interactions

871
Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
871

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Causal interaction in high frequency turbulence at the biosphere-atmosphere interface: Structural behavior.

Chaos (Woodbury, N.Y.)·2023
Same author

Causal interaction in high frequency turbulence at the biosphere-atmosphere interface: Structure-function coupling.

Chaos (Woodbury, N.Y.)·2023
Same author

Discerning the thermodynamic feasibility of the spontaneous coexistence of multiple functional vegetation groups.

Scientific reports·2020
Same author

Sustainability of soil organic carbon in consolidated gully land in China's Loess Plateau.

Scientific reports·2020
Same author

Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya.

PloS one·2019
Same author

Stochastic lattice-based modelling of malaria dynamics.

Malaria journal·2018

Related Experiment Video

Updated: Nov 27, 2025

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
06:45

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

6.4K

Bundled Causal History Interaction.

Peishi Jiang1, Praveen Kumar1

  • 1Ven Te Chow Hydrosystem Laboratory, Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801, USA.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces an information-based method to understand how groups of variables interact to influence outcomes in complex systems. The approach reveals synergistic effects, like those between cations and anions impacting stream pH.

Keywords:
bundled causal dynamicscomplex systeminformation measures

More Related Videos

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning
09:23

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning

Published on: March 21, 2025

1.6K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Related Experiment Videos

Last Updated: Nov 27, 2025

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
06:45

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

6.4K
JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning
09:23

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning

Published on: March 21, 2025

1.6K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Area of Science:

  • Complex Systems Science
  • Information Theory
  • Environmental Science

Background:

  • Complex systems exhibit emergent behaviors due to nonlinear interactions between components.
  • Understanding multivariate system dynamics requires analyzing inter-variable relationships.
  • A key challenge is quantifying causal influences between distinct variable subsets.

Purpose of the Study:

  • To develop an information-based framework for analyzing causal interactions between variable subsets.
  • To quantify the strength and nature of dependencies within complex systems.
  • To apply the framework to a real-world environmental system.

Main Methods:

  • Utilized a probabilistic graphical model to represent temporal interactions.
  • Employed partial information decomposition to measure interaction strengths.
  • Applied the method to analyze stream chemistry data.

Main Results:

  • The approach successfully delineated interactions between variable bundles.
  • Partial information decomposition revealed complex dependencies and memory effects.
  • Demonstrated cation and anion interactions as determinants of stream pH, highlighting synergistic effects.

Conclusions:

  • The developed information-based approach provides a robust method for studying group variable interactions.
  • The framework is broadly applicable to diverse complex systems.
  • This work lays the foundation for understanding emergent properties in multivariate systems.