Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Causality in Epidemiology01:21

Causality in Epidemiology

347
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...
347
Cause and Effect01:53

Cause and Effect

10.9K
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?
10.9K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

242
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:
242
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

235
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:
235
Correlation and Causation01:27

Correlation and Causation

37.5K
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...
37.5K
Probability Laws01:49

Probability Laws

40.7K
Overview
40.7K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

MODELING THE VISIBILITY DISTRIBUTION FOR RESPONDENT-DRIVEN SAMPLING WITH APPLICATION TO POPULATION SIZE ESTIMATION.

The annals of applied statistics·2026
Same author

Estimating Asymptomatic and Symptomatic Transmission of the COVID-19 First Few Cases in Selenge Province, Mongolia.

Influenza and other respiratory viruses·2024
Same author

Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting.

PloS one·2023
Same author

Modeling of networked populations when data is sampled or missing.

Metron·2023
Same journal

Incorporating external risk information with the Cox model under population heterogeneity: applications to trans-ancestry polygenic hazard scores.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

A Bayesian mixture model approach to examining neighbourhood social determinants of health in endometrial cancer care in Massachusetts.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

Professor Ian Hall's contribution to the Discussion of 'Some statistical aspects of the COVID-19 response' by Wood et al.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

Multivariate mixed models accounting for don't know options in ordinal data.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

A Bayesian zero-inflated spatially varying coefficients model for overdispersed binomial data.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2025
查看所有相关文章

相关实验视频

Updated: Jun 13, 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

7.9K

对随机网络的因果推理.

Duncan A Clark1, Mark S Handcock1

  • 1Department of Statistics & Data Science, University of California - Los Angeles, Los Angeles, CA, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|September 16, 2024
PubMed
概括
此摘要是机器生成的。

本研究为具有内源关系的网络引入了一种新的因果推理模型,考虑复杂的依赖关系和溢出效应. 该框架通过模拟进行验证,并应用于青少年吸烟行为.

关键词:
吉布斯测量方法 吉布斯测量方法有关因果关系的因果关系传染 传染 传染 传染干扰干扰是干扰的网络模型 网络模型溢出影响 溢出影响

更多相关视频

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
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

2.4K

相关实验视频

Last Updated: Jun 13, 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

7.9K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
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

2.4K

科学领域:

  • 社交网络分析分析
  • 因果推理的原因推理.
  • 统计建模 统计建模

背景情况:

  • 网络中的因果推理需要解决结果依赖性.
  • 治疗溢出和结果干扰是关键的挑战.
  • 现有的模型通常假设网络独立性或固定结构.

研究的目的:

  • 在具有内源结构的网络中开发一种用于因果推理的新型模型.
  • 共同建模关系和共变量生成过程.
  • 克服可分离性和固定网络假设的局限性.

主要方法:

  • 开发了内生网络结构和参与者共变量的联合模型.
  • 使用了指数式家族随机网络模型 (ERNM).
  • 采用贝叶斯框架进行潜在的基于结果的推断,并修改了采样交换算法.

主要成果:

  • 拟议的框架成功地模拟了内源网络.
  • 模拟研究证明了该方法的有效性.
  • 该模型有效地处理复杂的依赖关系和溢出效应.

结论:

  • 开发的框架为复杂网络设置中的因果推理提供了强大的方法.
  • 它为限制性网络假设提供了一个灵活的替代方案.
  • 这种方法对于研究社交网络中的青少年吸烟等现象是有价值的.