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

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Naturalistic Observations02:30

Naturalistic Observations

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...
What is an Experiment?01:12

What is an Experiment?

An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
Causality in Epidemiology01:21

Causality in Epidemiology

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...
Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...

You might also read

Related Articles

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

Sort by
Same author

The ARDS, Pneumonia, and Sepsis (APS) Consortium: Rationale, Design, and Feasibility of a National Platform for Phenotyping Critical Illness Syndromes.

Chest·2026
Same author

Antifungal use with and without fungal diagnoses in septic shock across U.S. hospitals, 2022-2024.

medRxiv : the preprint server for health sciences·2026
Same author

Phenobarbital as an Adjuvant to benzodiazepines when compared to Single-agent benzodiazepine Treatment (usual care) for Alcohol withdrawal syndrome in the intensive care unit (PASTA): a study protocol.

Pilot and feasibility studies·2026
Same author

Slow Life Support for Imminently Dying Patients.

JAMA·2026
Same author

Prone Positioning in a North American Cohort of Hypoxemic Patients on Mechanical Ventilation.

Critical care medicine·2026
Same author

Effects of an early restrictive versus liberal fluid strategy on long-term patient-centered outcomes in sepsis-induced hypotension: an open-label, randomized controlled trial.

American journal of respiratory and critical care medicine·2026

Related Experiment Video

Updated: May 10, 2026

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies
07:43

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

Published on: August 4, 2023

Instrumental variable analyses. Exploiting natural randomness to understand causal mechanisms.

Theodore J Iwashyna1, Edward H Kennedy

  • 1Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan, USA. tiwashyn@umich.edu

Annals of the American Thoracic Society
|June 28, 2013
PubMed
Summary

Instrumental variable analysis uses randomness in treatment assignment to establish causality from observational data. This method helps distinguish true treatment effects from mere associations, crucial for social science research.

Related Experiment Videos

Last Updated: May 10, 2026

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies
07:43

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

Published on: August 4, 2023

Area of Science:

  • Social Sciences
  • Epidemiology
  • Biostatistics

Background:

  • Observational studies often show associations, not causation.
  • Establishing treatment effects requires robust methodologies.
  • Instrumental variable analysis offers a solution for causal inference.

Purpose of the Study:

  • Explain the logic of instrumental variable analysis in non-mathematical terms.
  • Provide examples of its application.
  • Equip readers with criteria to evaluate the quality of instrumental variable studies.

Main Methods:

  • Utilizes naturally occurring randomness in treatment assignment.
  • Employs an instrumental variable that influences treatment but not the outcome directly.
  • Focuses on observational data to infer causal relationships.

Main Results:

  • Instrumental variable analysis can provide strong evidence for treatment causality.
  • The validity of the analysis depends on the instrumental variable's properties.
  • Key questions guide the evaluation of evidence quality.

Conclusions:

  • Instrumental variable analysis is a powerful tool for causal inference in observational studies.
  • Understanding the core assumptions is vital for correct application and interpretation.
  • Critical appraisal using specific questions enhances the reliability of findings.