ArticleEpidemiology (Cambridge, Mass.)2020
A Graphical Catalog of Threats to Validity: Linking Social Science with Epidemiology.
Article in Epidemiology (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating the health effects of place-based slum upgrading physical environment interventions: A systematic review (2012-2018).Social science & medicine (1982) · 2020Pooled it
- Construct validity in cross-cultural, developmental research: challenges and strategies for improvement.Evolutionary human sciences · 2025Article
- Performance of five dynamic models in predicting tuberculosis incidence in three prisons in Thailand.PloS one · 2025Article
- Theorizing epidemiology, the stories bodies tell, and embodied truths: a status update on contending 21International journal of social determinants of health and health services · 2024Review
- The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective.Life (Basel, Switzerland) · 2024Article
- Article
- Secondary use of routinely collected administrative health data for epidemiologic research: Answering research questions using data collected for a different purpose.International journal of population data science · 2024Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
- Toward a causal model of chronic back pain: Challenges and opportunities.Frontiers in computational neuroscience · 2022Review
- Triangulating Evidence through the Inclusion of Genetically Informed Designs.Cold Spring Harbor perspectives in medicine · 2021Review
- First trimester mechanisms of gestational sac placental and foetal teratogenicity: a framework for birth cohort studies.Human reproduction update · 2021Review
- The Taboo Against Explicit Causal Inference in Nonexperimental Psychology.Perspectives on psychological science : a journal of the Association for Psychological Science · 2020Article
- Comparisons of Three Measures of Maternal Engagement Activities in the Neonatal Intensive Care Unit.Nursing researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Directed acyclic graphs (DAGs), a prominent tool for expressing assumptions in epidemiologic research, are most useful when the hypothetical data generating structure is correctly encoded. Understanding a study's data generating structure and translating that data structure into a DAG can be challenging, but these skills are often glossed over in training. Campbell and Stanley's framework for causal inference has been extraordinarily influential in social science training programs but has received less attention in epidemiology. Their work, along with subsequent revisions and enhancements based on practical experience conducting empirical studies, presents a catalog of 37 threats to validity describing reasons empirical studies may fail to deliver causal effects. We interpret most of these threats to study validity as suggestions for common causal structures. Threats are organized into issues of statistical conclusion validity, internal validity, construct validity, or external validity. To assist epidemiologists in drawing the correct DAG for their application, we map the correspondence between threats to validity and epidemiologic concepts that can be represented with DAGs. Representing these threats as DAGs makes them amenable to formal analysis with d-separation rules and breaks down cross-disciplinary language barriers in communicating methodologic issues.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.