Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
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...
1.8K
Cause and Effect01:53

Cause and Effect

12.6K
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?
12.6K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.4K
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:
1.4K
Theory of Attribution I: Correspondent Inference Theory01:15

Theory of Attribution I: Correspondent Inference Theory

641
Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
641
Interference: Path Lengths01:10

Interference: Path Lengths

2.3K
Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
Two special sources may be considered when they are in phase. This can be easily achieved by feeding the two sources from the same source. An example would be synchronizing the two speakers by feeding them with the same source, such as the sound waves produced by a tuning fork. This setup ensures that the two sources have the same frequency and are...
2.3K
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

1.2K
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:
1.2K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Clarifying the 'set to zero' approach for time-varying prenatal exposures.

International journal of epidemiology·2026
Same author

Rejoinder to the discussion on 'Causal inference with misspecified network interference structure'.

Biometrics·2026
Same author

Learn-As-you-GO (LAGO) trials: optimizing treatments and preventing trial failure through ongoing learning.

Biometrics·2025
Same author

The subtype-free average causal effect for heterogeneous disease etiology.

Biometrics·2025
Same author

Ambient temperature exposure and rapid infant weight gain.

International journal of epidemiology·2024
Same author

A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies.

Biometrics·2022
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
関連記事をすべて見る

関連する実験動画

Updated: Feb 24, 2026

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

8.5K

ネットワーク干渉構造の誤指定を伴う因果推論

Bar Weinstein1, Daniel Nevo1

  • 1Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, 6997801, Israel.

Biometrics
|February 23, 2026
PubMed
まとめ
この要約は機械生成です。

因果推論におけるネットワークの誤指定は結果に偏りを生じさせる可能性がある。本研究では、テストされた複数のネットワークのいずれかが正しい場合でも偏りがない頑健な推定量を提案し、誤ったネットワーク仮定による偏りを軽減する。

キーワード:
SUTVA曝露マッピングマルチレイヤーネットワークネットワーク実験スピルオーバー

さらに関連する動画

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

3.4K
A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

14.1K

関連する実験動画

Last Updated: Feb 24, 2026

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

8.5K
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

3.4K
A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

14.1K

科学分野:

  • 因果推論
  • ネットワーク分析
  • ソーシャルネットワーク分析

背景:

  • 多くの分野で、単位間の干渉は一般的である。
  • 干渉パターンはしばしばネットワークを用いてモデル化される。
  • 正確なネットワーク指定は重要であるが、困難である。

研究 の 目的:

  • 因果効果推定におけるネットワークの誤指定の結果を調査する。
  • ネットワークの誤指定に対して頑健な新しい推定量を開発する。

主な方法:

  • 誤指定されたネットワークに対するバイアス限界の導出。
  • 誘導された曝露確率を用いたバイアスの定量化。
  • 複数のネットワークを活用する新しい推定量の開発。

主要な成果:

  • ネットワークの乖離に伴い、推定バイアスが増加する。
  • 提案された推定量は、少なくとも1つのネットワークが正しい場合、偏りがない。
  • シミュレーションとフィールド実験により、推定量の有用性が実証される。

結論:

  • ネットワークの誤指定は、因果推論において重大な課題をもたらす。
  • 提案されたマルチネットワーク推定量は、ネットワーク指定エラーに対する頑健性を提供する。
  • このアプローチは、ネットワーク設定における因果効果推定の信頼性を高める。