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Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

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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 - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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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:
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Correlation and Causation01:27

Correlation and Causation

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

Criteria for Causality: Bradford Hill Criteria - I

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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:
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Cause and Effect01:53

Cause and Effect

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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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Related Experiment Video

Updated: Apr 16, 2026

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
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CauseMap: fast inference of causality from complex time series.

M Cyrus Maher1, Ryan D Hernandez2

  • 1Department of Epidemiology and Biostatistics, University of California , San Francisco, CA , USA.

Peerj
|March 18, 2015
PubMed
Summary

CauseMap introduces convergent cross mapping (CCM) for identifying causality in biomedical time series data. This open-source tool aids in understanding complex biological systems and advancing personalized medicine.

Keywords:
CausalityDynamical systemsOpen source softwarePersonalized medicineTime series methods

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Area of Science:

  • Biomedical research
  • Dynamical systems theory
  • Computational biology

Background:

  • Establishing causal relationships in complex biological systems is challenging due to non-linearity and data limitations.
  • Advancements in data collection, particularly from wearable devices, are increasing the availability of biomedical time series data.
  • There is a critical need for open-source software to analyze this growing volume of time series data for biomedical insights.

Purpose of the Study:

  • To present CauseMap, the first open-source implementation of convergent cross mapping (CCM).
  • To provide a robust method for establishing causality from long time series data in biomedical research.
  • To develop a tool applicable to personalized medicine by analyzing individual time series data.

Main Methods:

  • Convergent Cross Mapping (CCM), based on Takens' Theorem from dynamical systems theory.
  • Reconstruction of high-dimensional system dynamics from single-variable time series.
  • Inferring causal relationships by testing predictive power between reconstructed time series, even with feedback loops.

Main Results:

  • CauseMap offers a model-free and robust approach to causality detection, resilient to unmeasured confounding.
  • The CCM method implemented in CauseMap can establish the directionality of causation.
  • The software is implemented in Julia, ensuring high performance and platform independence.

Conclusions:

  • CauseMap is an efficient, state-of-the-art open-source tool for detecting causality in time series data.
  • This tool has significant potential to advance biomedical research by uncovering complex causal interactions.
  • CauseMap is poised to be a valuable resource for personalized medicine applications.