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 Experiment Videos

Estimating causal effects from epidemiological data.

Miguel A Hernán1, James M Robins

  • 1Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. miguel_hernan@post.harvard.edu

Journal of Epidemiology and Community Health
|June 23, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Methodological Approaches to Real-World Evidence Generation for Glucagon-like Peptide-1-Based Therapies: Synopsis of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

Annals of internal medicine·2026
Same author

Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population.

Statistics in medicine·2026
Same author

Statins and risk of cardiovascular disease: Emulating a primary prevention trial in breast cancer survivors.

Journal of the National Cancer Institute·2026
Same author

Long-term effects of colonoscopy screening on colorectal cancer incidence and mortality: a multicountry, population-based randomised controlled trial.

Lancet (London, England)·2026
Same author

Extending inferences from a randomized trial to trial-eligible and treatment-candidate target populations: examples of generalizability and transportability.

Epidemiology (Cambridge, Mass.)·2026
Same author

The TARGET guideline for reporting observational studies of interventions.

Nature medicine·2026
Same journal

Comparative social costs of six early years disadvantages: a birth cohort microsimulation study.

Journal of epidemiology and community health·2026
Same journal

Sociodemographic inequalities in non-small cell lung cancer (NSCLC) novel treatments: a national population-based cohort study.

Journal of epidemiology and community health·2026
Same journal

Secular trends in primary care utilisation for mental health problems: a Norwegian register-based population-wide study.

Journal of epidemiology and community health·2026
Same journal

Guide to recent advances in difference-in-differences methodology for population health studies.

Journal of epidemiology and community health·2026
Same journal

From early to fewer first births: ADHD and family formation among young adults.

Journal of epidemiology and community health·2026
Same journal

10-year mortality among first-time mothers involved in family court care proceedings in England: cohort study using linked administrative hospital, mortality and family court records.

Journal of epidemiology and community health·2026
See all related articles

Observational studies can estimate causal effects when exposed and unexposed groups are exchangeable. This review covers methods like standardisation and inverse probability weighting for causal inference from observational data.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Randomised experiments allow direct interpretation of association as causation due to exchangeability.
  • Observational studies often lack exchangeability, making direct causal interpretation of associations challenging.
  • Despite limitations, observational research is crucial for causal inference when experiments are not feasible.

Purpose of the Study:

  • To review the condition enabling causal effect estimation from observational data.
  • To describe standardisation and inverse probability weighting for estimating population causal effects.
  • To simplify the explanation for dichotomous variables, excluding sampling variability.

Main Methods:

  • Review of conditions for causal inference in observational studies.

Related Experiment Videos

  • Explanation of standardisation for estimating causal effects.
  • Description of inverse probability weighting for estimating causal effects.
  • Generalisation of inverse probability weighting provided in the appendix.

    • Main Results:

    • Identifies the condition under which observational data permits causal effect estimation.
    • Demonstrates the application of standardisation and inverse probability weighting.
    • Provides a generalized method for inverse probability weighting.

    Conclusions:

    • Causal effects can be estimated from observational data if exchangeability is met.
    • Standardisation and inverse probability weighting are key methods for this estimation.
    • The methods discussed are essential for robust causal inference in observational research.