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

Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

555
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:
555
Causality in Epidemiology01:21

Causality in Epidemiology

942
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...
942
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

694
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:
694
Correlation and Causation01:27

Correlation and Causation

39.7K
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...
39.7K
Continuity Equation01:20

Continuity Equation

1.0K
The total amount of current flowing per unit cross-sectional area is called the current density. Hence, the current passing through a cross-sectional area can be written as the surface integral of the current density.
1.0K
Classification of Signals01:30

Classification of Signals

940
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
940

You might also read

Related Articles

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

Sort by
Same author

Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics.

Nature communications·2026
Same author

Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.

Nature communications·2026
Same author

A general framework for neural delay differential equations with various delay types.

Chaos (Woodbury, N.Y.)·2026
Same author

Optimizing disorder with machine learning to harness phase synchronization.

Chaos (Woodbury, N.Y.)·2026
Same author

Controlling severe atopic dermatitis dynamics.

Chaos (Woodbury, N.Y.)·2026
Same author

Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner.

Nature communications·2026
Same journal

Predicting 1-Year Renal Outcomes in Patients with Diabetic Kidney Disease in CKD Stages 3 to 4: A Multimodal Machine Learning Approach Fusing Clinical Composites and Pathology Images.

Research (Washington, D.C.)·2026
Same journal

Antioxidant Nanozymes: From Rational Design to Biomedical Applications.

Research (Washington, D.C.)·2026
Same journal

Quantum-Inspired Fast Algorithm and Circuit Realization for Constrained Combinatorial Optimization Problem.

Research (Washington, D.C.)·2026
Same journal

Monocyte-Derived LGMN<sup>+</sup> Macrophages Divert Lung Injury Outcomes toward Fibrosis through Matrix Remodeling.

Research (Washington, D.C.)·2026
Same journal

From Isolation to Collaboration: Data Trading Mechanism in the Era of Large Language Model Democratization.

Research (Washington, D.C.)·2026
Same journal

Ultrasensitive In Vivo Imaging of Adoptive Immune Cell Distribution and Expansion Using Second Near-Infrared Conjugated Oligoelectrolyte Probes.

Research (Washington, D.C.)·2026
See all related articles

Related Experiment Video

Updated: Sep 22, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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

Published on: August 7, 2017

8.0K

Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately.

Xiong Ying1,2,3, Si-Yang Leng2,4, Huan-Fei Ma5

  • 1School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China.

Research (Washington, D.C.)
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

We introduce a new framework for detecting causation in complex systems by analyzing scaling laws of system continuity. This method accurately identifies causal relationships and their strength, outperforming existing techniques.

More Related Videos

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.8K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

Related Experiment Videos

Last Updated: Sep 22, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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

Published on: August 7, 2017

8.0K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.8K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

Area of Science:

  • Complex systems science
  • Nonlinear dynamics
  • Causality analysis

Background:

  • Data-based detection of causation is crucial for understanding complex, nonlinear dynamical systems.
  • Existing cross-map techniques measure causality indirectly via map smoothness.
  • A more direct and robust method for causal inference is needed.

Purpose of the Study:

  • To develop a general framework for understanding dynamical causal mechanisms.
  • To establish a causality detection method consistent with the natural interpretation of causality.
  • To improve the accuracy, reliability, and efficiency of causal inference in complex systems.

Main Methods:

  • Developed a novel framework based on measuring the scaling law for dynamical system continuity.
  • Replaced conventional cross-map smoothness measurements with direct continuity scaling analysis.
  • Validated the framework using model complex systems and real-world datasets.

Main Results:

  • The continuity scaling-based framework accurately detects causation and quantifies its strength.
  • This new method demonstrates superior performance compared to existing representative techniques.
  • The framework is rigorously established and applicable to general complex dynamical systems.

Conclusions:

  • The continuity scaling approach offers a robust and efficient method for causal discovery.
  • This framework advances the comprehensive understanding of dynamical causal mechanisms.
  • The findings have broad implications for science, engineering, and data analysis.