Cited by 165Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyMeta-analysis and systematic reviews

The DOI matches its Crossref record; 33 of the 34 references it cites were found in a registry or an index.

Target Validity and the Hierarchy of Study Designs

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Abstract

In recent years, increasing attention has been paid to problems of external validity, specifically to methodological approaches for both quantitative generalizability and transportability of study results. However, most approaches to these issues have considered external validity separately from internal validity. Here we argue that considering either internal or external validity in isolation may be problematic. Further, we argue that a joint measure of the validity of an effect estimate with respect to a specific population of interest may be more useful: We call this proposed measure target validity. In this work, we introduce and formally define target bias as the total difference between the true causal effect in the target population and the estimated causal effect in the study sample, and target validity as target bias = 0. We illustrate this measure with a series of examples and show how this measure may help us to think more clearly about comparisons between experimental and nonexperimental research results. Specifically, we show that even perfect internal validity does not ensure that a causal effect will be unbiased in a specific target population.

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Research works printed but not linked: 4

  1. Bareinboim, 2014, Advances in Neural Information Processing Systems 26., 136
  2. Bareinboim, 2015, Advances in Neural Information Processing Systems 27., 280
  3. Pearl, 2011, 2011 JSM Proceedings: Papers Presented at the Joint Statistical Meetings, Miami Beach, Florida, July 30–August 4, 2011, and Other ASA-Sponsored Conferences, 157
  4. Bareinboim, 2014, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence and the Twenty-Sixth Innovative Applications of Artificial Intelligence Conference., 2410

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Metadata

Authors
Westreich, D., Edwards, J. K., Lesko, C. R., Cole, S. R., & Stuart, E. A.
Affiliations
  • University of North Carolina at Chapel Hill (US)
  • Johns Hopkins University (US)
Corresponding author
Westreich, Daniel
Venue
American Journal of Epidemiology
Published
2018-10-09
Volume
188
Issue
2
Pages
438-443
ISSN
  • 0002-9262 (ISSN-L)
  • 1476-6256
OpenAlex
W2900846215
Cited by
165
References
34
Citations by year
2026: 15 · 2025: 15 · 2024: 28 · 2023: 26 · 2022: 24 · 2021: 21 · 2020: 23 · 2019: 12
FWCI
11.62
Type
journal article
Language
en
Open access
Locations
  • American Journal of Epidemiology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
  • UNC Libraries
Funders
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development
  • National Institutes of Health (DP2-HD084070)
  • National Institute of Allergy and Infectious Diseases (R01 AI100654)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
target bias, generalizability, internal validity, causal effect estimation, target population, transportability, causal inference
MeSH terms
Epidemiologic Methods, Humans, Reproducibility of Results, Bias, Causality

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