Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

313
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...
313
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

83
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
83
Crossover Experiments01:16

Crossover Experiments

2.7K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
2.7K
Two-Way ANOVA01:17

Two-Way ANOVA

2.6K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.6K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

204
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:
204
Experimental Designs01:16

Experimental Designs

11.1K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
11.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

How work hours affect well-being: A target trial emulation.

PloS one·2026
Same author

Target trial emulation shows that supported causal effects of religious attendance on well-being are selective.

Evolutionary human sciences·2026
Same author

Three evolutionary radiations shaped the evolution of global religious diversity.

Evolutionary human sciences·2025
Same author

Methods in causal inference. Part 4: confounding in experiments.

Evolutionary human sciences·2024
Same author

Methods in causal inference. Part 3: measurement error and external validity threats.

Evolutionary human sciences·2024
Same author

Methods in causal inference. Part 1: causal diagrams and confounding.

Evolutionary human sciences·2024

Related Experiment Video

Updated: Jun 6, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.9K

Methods in causal inference. Part 2: Interaction, mediation, and time-varying treatments.

Joseph A Bulbulia1

  • 1Victoria University of Wellington, Wellington, New Zealand.

Evolutionary Human Sciences
|November 27, 2024
PubMed
Summary

Clarifying causal inference for moderation, mediation, and longitudinal growth is crucial. Causal directed acyclic graphs and single world intervention graphs help define and identify causal estimands, revealing limitations in common statistical methods.

Keywords:
DAGsSWIGsmediationmoderationtime-varying treatments

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K
Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
06:45

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

6.2K

Related Experiment Videos

Last Updated: Jun 6, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.9K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K
Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
06:45

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

6.2K

Area of Science:

  • Causal inference
  • Human sciences
  • Statistical modeling

Background:

  • Widespread confusion exists regarding the analysis of moderation, interaction, mediation, and longitudinal growth in human sciences.
  • Accurate causal inference requires clear definition and identification of causal estimands.

Purpose of the Study:

  • To clarify concepts of moderation, interaction, mediation, and longitudinal growth.
  • To elucidate identification workflows using causal directed acyclic graphs (DAGs) and single world intervention graphs (SWIGs).
  • To expose limitations of common statistical methods in recovering causal quantities.

Main Methods:

  • Defining causal estimands via counterfactual contrasts on an appropriate scale.
  • Employing causal DAGs and SWIGs to illustrate identification.
  • Analyzing the suitability of multi-level regressions and structural equation models for causal inference.

Main Results:

  • Common statistical methods like multi-level regressions and structural equation models often fail to recover desired causal quantities, especially with multiple treatments.
  • Properly framing causal questions is essential for accurate analysis.

Conclusions:

  • The study highlights the limitations of popular statistical methods for complex causal questions in the human sciences.
  • Researchers are guided towards a clearer understanding of causal inference for interaction, mediation, and time-varying treatments.