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

Strategies for Assessing and Addressing Confounding

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...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Community Based Intervention01:30

Community Based Intervention

Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...
Nursing Interventions II: Selecting and Classifying the Nursing Interventions01:29

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:

You might also read

Related Articles

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

Sort by
Same author

Transportability to the European Population of Efficacy of Belumosudil as Compared With Physician's Choice of Best Available Therapy for the Treatment of Chronic Graft Versus Host Disease.

Transplantation and cellular therapy·2026
Same author

Methodological and regulatory considerations for causal AI in drug development.

NPJ digital medicine·2026
Same author

Discovery of critical thresholds in mixed exposures and estimation of policy intervention effects.

Journal of causal inference·2026
Same author

Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions.

Journal of causal inference·2026
Same author

Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer.

Proceedings of machine learning research·2025
Same author

Efficacy and safety of belumosudil as compared with best available therapy for the treatment of cGVHD in the United States.

Blood advances·2025

Related Experiment Videos

Population intervention causal effects based on stochastic interventions.

Iván Díaz Muñoz1, Mark van der Laan

  • 1Division of Biostatistics, School of Public Health, 101 Haviland Hall, University of California at Berkeley, Berkeley, California 94720-7358, USA. ildiazm@berkeley.edu

Biometrics
|October 8, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new causal parameter for interventions with stochastic assignments, developing statistical methods like augmented IPTW and TMLE for accurate estimation. These methods improve causal inference in complex public health scenarios.

Related Experiment Videos

Area of Science:

  • Causal inference
  • Statistical modeling
  • Public health research

Background:

  • Estimating intervention effects often uses nonparametric structural equation models with deterministic assignments.
  • Existing methods may not fully capture stochastic exposure assignments in real-world interventions.

Purpose of the Study:

  • To define a novel causal parameter accommodating stochastically assigned exposures.
  • To develop and evaluate statistical estimators for this new causal parameter.

Main Methods:

  • Defined a new causal parameter for stochastic interventions.
  • Established the identifying statistical parameter.
  • Developed Inverse Probability of Treatment Weighting (IPTW), Augmented IPTW (A-IPTW), and Targeted Maximum Likelihood Estimators (TMLE).
  • Conducted a simulation study to assess estimator properties.

Main Results:

  • Demonstrated the double robustness of A-IPTW and TMLE estimators.
  • Validated the developed estimators through simulation.
  • Presented an application using physical activity data.

Conclusions:

  • The new causal parameter and developed estimators enhance causal inference for stochastic interventions.
  • A-IPTW and TMLE offer robust estimation strategies.
  • Findings are applicable to public health interventions with complex exposure patterns.