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Related Concept Videos

Criteria for Causality: Bradford Hill Criteria - II01:28

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 - I01:30

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 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...
Correlation and Causation01:27

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...
Reason and Intuition01:37

Reason and Intuition

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 brain can only use...
Counterfactual Thinking01:19

Counterfactual Thinking

Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in human cognition.Types of...

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

Published on: July 18, 2014

Causality, mediation and time: a dynamic viewpoint.

Odd O Aalen1, Kjetil Røysland, Jon Michael Gran

  • 1University of Oslo Norway.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|November 30, 2012
PubMed
Summary
This summary is machine-generated.

Causality operates in time, requiring a mechanistic understanding often overlooked in causal modeling. This study proposes a dynamic, mechanistic approach to causality, improving upon traditional counterfactual methods.

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Area of Science:

  • Causal inference
  • Systems science
  • Epidemiology

Background:

  • Traditional causal modeling often neglects temporal dynamics.
  • Counterfactual definitions of effects may be impractical due to manipulation constraints.

Purpose of the Study:

  • To integrate time dynamics into causal modeling.
  • To propose a mechanistic, systems-based understanding of causality.
  • To compare mechanistic and counterfactual approaches.

Main Methods:

  • Development of a mechanistic framework for causality.
  • Utilizing local independence graphs and dynamic path analysis.
  • Application to real-world data from the Swiss HIV Cohort Study.

Main Results:

  • Demonstration of how time dynamics align with a mechanistic view of causality.
  • Graphical methods provide clear overviews of dynamic causal relations.
  • Mechanistic causality offers a potentially more practical approach than counterfactuals.

Conclusions:

  • A time-dynamic, mechanistic perspective enhances causal modeling.
  • Graphical tools aid in understanding complex causal systems.
  • This framework provides a robust alternative for analyzing causal effects in observational studies.