ArticleInternational journal of epidemiology2016
The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology.
Article in International journal of epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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
104 citing papers in PubMed, 251 citations in OpenAlex.
- Trial
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- Whose Knowledge Counts? Considering Gender, Sexuality, and Race in Description, Prediction, and Causal Inference in Oral Epidemiology.Advances in dental research · 2026Article
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- Methodological approaches to structural change: epidemiology and the case for reparations.American journal of epidemiology · 2025Article
- Considerations for Causal Inference Studies.Respirology (Carlton, Vic.) · 2025Article
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- Inconsistent consistency: evaluating the well-defined intervention assumption in applied epidemiological research.International journal of epidemiology · 2025Review
- Evolving racial/ethnic disparities in AML survival in the novel therapy era.Blood advances · 2025Article
- Invited commentary: policies that support working parents and gender health equity-needed research and methodological challenges.American journal of epidemiology · 2025Article
- Epidemiology: a partnership of technical expertise and clinical insight.BMJ medicine · 2025Article
- Precision psychiatry needs causal inference.Acta neuropsychiatrica · 2024Review
- 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
44 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
'Causal inference', in 21st century epidemiology, has notably come to stand for a specific approach, one focused primarily on counterfactual and potential outcome reasoning and using particular representations, such as directed acyclic graphs (DAGs) and Bayesian causal nets. In this essay, we suggest that in epidemiology no one causal approach should drive the questions asked or delimit what counts as useful evidence. Robust causal inference instead comprises a complex narrative, created by scientists appraising, from diverse perspectives, different strands of evidence produced by myriad methods. DAGs can of course be useful, but should not alone wag the causal tale. To make our case, we first address key conceptual issues, after which we offer several concrete examples illustrating how the newly favoured methods, despite their strengths, can also: (i) limit who and what may be deemed a 'cause', thereby narrowing the scope of the field; and (ii) lead to erroneous causal inference, especially if key biological and social assumptions about parameters are poorly conceived, thereby potentially causing harm. As an alternative, we propose that the field of epidemiology consider judicious use of the broad and flexible framework of 'inference to the best explanation', an approach perhaps best developed by Peter Lipton, a philosopher of science who frequently employed epidemiologically relevant examples. This stance requires not only that we be open to being pluralists about both causation and evidence but also that we rise to the challenge of forging explanations that, in Lipton's words, aspire to 'scope, precision, mechanism, unification and simplicity'.
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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.