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Related Concept Videos

Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
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Causality in Epidemiology01:21

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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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Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Criteria for Causality: Bradford Hill Criteria - II01:28

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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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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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Actor-Observer Effect

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The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
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Related Experiment Video

Updated: Jan 26, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Using Mendelian randomisation to assess causality in observational studies.

Panagiota Pagoni1,2, Niki L Dimou3, Neil Murphy3

  • 1Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

Evidence-Based Mental Health
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Mendelian randomization (MR) assesses causal links between exposures and diseases. Validating MR assumptions is vital for accurate risk factor analysis in medical research.

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Schizophrenia and psychotic disorders

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Area of Science:

  • Epidemiology
  • Statistical Genetics

Background:

  • Mendelian randomization (MR) is a powerful causal inference technique.
  • It leverages genetic variants as instrumental variables to estimate causal effects.
  • Understanding MR assumptions is critical for reliable results.

Purpose of the Study:

  • To present the core assumptions of Mendelian randomization.
  • To outline statistical methods for estimating causal effects.
  • To describe approaches for addressing violations of MR assumptions.

Main Methods:

  • Discusses key assumptions for MR studies.
  • Details statistical methods for two-sample MR using summary data (Wald ratio, IVW, ML).
  • Covers methods for detecting and adjusting for assumption violations (MR-Egger, weighted median) and heterogeneity.

Main Results:

  • Illustrates MR application using body mass index and psychiatric disorders (bipolar, schizophrenia, MDD).
  • Emphasizes evaluating multiple methods for robust causal effect estimation.
  • Demonstrates the influence of heterogeneity on causal effect estimates.

Conclusions:

  • Mendelian randomization is a valuable tool for causal inference in medical research.
  • Rigorous assessment of underlying assumptions is essential for valid MR study interpretation.