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

7.0K
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...
7.0K
Causality in Epidemiology01:21

Causality in Epidemiology

436
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...
436
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

330
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:
330
Censoring Survival Data01:09

Censoring Survival Data

108
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
108
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

33.4K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
33.4K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

200
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
200

You might also read

Related Articles

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

Sort by
Same author

Association of PFAS and Metals with Cardiovascular Disease Risk: Exploring the Mediating Effect of Diet.

Environments (Basel, Switzerland)·2025
Same author

Peritraumatic C-reactive protein levels predict pain outcomes following traumatic stress exposure in a sex-dependent manner.

medRxiv : the preprint server for health sciences·2024
Same author

A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA).

Psychometrika·2024
Same author

Selecting scaling indicators in structural equation models (sems).

Psychological methods·2022
Same author

An introduction to model implied instrumental variables using two stage least squares (MIIV-2SLS) in structural equation models (SEMs).

Psychological methods·2021
Same author

Trajectories of Subjective Health: Testing Longitudinal Models for Self-rated Health From Adolescence to Midlife.

Demography·2021
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

Related Experiment Video

Updated: Jul 11, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Causal inference with binary treatments from randomization versus binary treatments from categorization.

Kenneth A Bollen1

  • 1Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill.

Psychological Methods
|November 13, 2023
PubMed
Summary
This summary is machine-generated.

Causal inference methods often treat binary treatments uniformly. This study shows distinct origins of binary treatments require different analytical approaches to prevent biased causal effect estimations.

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Related Experiment Videos

Last Updated: Jul 11, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Area of Science:

  • Causal Inference
  • Statistical Methodology
  • Observational Studies

Background:

  • Potential outcomes (POs), directed acyclic graphs (DAGs), and structural equation models (SEMs) are key causal inference frameworks.
  • These methods predominantly treat treatment as binary, overlooking variations in treatment magnitude.
  • Binary treatments often arise from randomized experiments or categorized continuous treatments, particularly in observational studies.

Purpose of the Study:

  • To demonstrate that binary treatment variables with different origins necessitate distinct analytical treatments.
  • To highlight the increased likelihood of biased causal inferences when distinct binary treatments are analyzed uniformly.
  • To advocate for the integrated use of POs, DAGs, and SEMs for a comprehensive understanding of binary treatment complexities.

Main Methods:

  • Derivation of new analytic results concerning the differential treatment of binary variables based on their origin.
  • Simulation studies to illustrate the impact of uniform versus distinct treatment analysis.
  • Application of an empirical example to showcase practical implications.

Main Results:

  • Binary treatment variables originating from different sources (e.g., randomized vs. categorized continuous) require separate analytical considerations.
  • Failing to differentiate binary treatments based on origin can lead to biased causal effect estimates.
  • Combining POs, DAGs, and SEMs provides a robust framework for identifying and addressing issues with binary treatments.

Conclusions:

  • Researchers must carefully consider the origin of binary treatment variables when conducting causal inference.
  • Adopting distinct analytical strategies for differently sourced binary treatments is crucial for accurate causal effect estimation.
  • Integrated application of POs, DAGs, and SEMs enhances the reliability of causal analyses involving binary treatments.