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 - II01:28

Criteria for Causality: Bradford Hill Criteria - II

549
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:
549
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

462
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:
462
Causality in Epidemiology01:21

Causality in Epidemiology

705
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...
705
Deductive Reasoning01:16

Deductive Reasoning

57.5K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
57.5K
Inductive Reasoning00:59

Inductive Reasoning

61.9K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
61.9K
Reason and Intuition01:37

Reason and Intuition

6.6K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.6K

You might also read

Related Articles

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

Sort by
Same author

Intravenous transplantation of mesenchymal stem cells improves cardiac performance after acute myocardial ischemia in female rats.

Transplant international : official journal of the European Society for Organ Transplantation·2006
Same author

[Effects of mechanical tensile stress on the expression of ICAM-1 mRNA in osteoblasts differentiated from rBMSCs].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2006
Same author

[Effects of osteoporosis on experimental tooth movement in aged rats].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2006
Same author

MCALIGN2: faster, accurate global pairwise alignment of non-coding DNA sequences based on explicit models of indel evolution.

BMC bioinformatics·2006
Same author

[Managements of masked mastoiditis].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2006
Same author

Neuronal SIRT1 activation as a novel mechanism underlying the prevention of Alzheimer disease amyloid neuropathology by calorie restriction.

The Journal of biological chemistry·2006

Related Experiment Video

Updated: Aug 28, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K

DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena.

Jun Wang, Klaus Mueller

    IEEE Transactions on Visualization and Computer Graphics
    |September 19, 2022
    PubMed
    Summary

    This study introduces visual analytics for discovering time-delayed causal relationships in observational data. The DOMINO system integrates human insight with logic-based causality to reveal temporal causal networks.

    Area of Science:

    • Data Science
    • Causality Research
    • Visual Analytics

    Background:

    • Current visual analytics for causality often rely on static, counterfactual models.
    • These models overlook the critical role of time delays in causal inference.
    • Deriving causality from observational time-series data is challenging and benefits from human expertise.

    Purpose of the Study:

    • To develop visual analytics methods for discovering time-delayed causal relations.
    • To enable human analysts to actively participate in causal discovery from time-series data.
    • To construct temporal causal networks by aggregating cause-effect relationships.

    Main Methods:

    • Leveraging logic-based causality to test potential causes and measure influence.
    • Incorporating human insight to resolve ambiguities and errors in causal discovery.

    More Related Videos

    Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills
    09:27

    Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills

    Published on: January 19, 2024

    1.3K
    Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
    11:33

    Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

    Published on: July 18, 2014

    43.1K

    Related Experiment Videos

    Last Updated: Aug 28, 2025

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
    13:00

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

    Published on: January 23, 2017

    10.0K
    Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills
    09:27

    Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills

    Published on: January 19, 2024

    1.3K
    Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
    11:33

    Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

    Published on: July 18, 2014

    43.1K
  • Developing a prototype system (DOMINO) for interactive temporal causal network analysis.

    • Main Results:

    • Demonstrated the effectiveness of the proposed methods through case studies with real-world datasets.
    • The DOMINO system successfully integrated human analysts in the causal discovery process.
    • Evaluations confirmed the utility of the system in practical scientific scenarios.

    Conclusions:

    • The proposed visual analytics approach effectively identifies time-delayed causal relationships.
    • Human-in-the-loop systems like DOMINO enhance causal discovery from observational time-series data.
    • This work facilitates the construction of dynamic temporal causal networks.