ReviewEpidemiology (Cambridge, Mass.)2014
Causal models and learning from data: integrating causal modeling and statistical estimation.
Review in Epidemiology (Cambridge, Mass.), 2014. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
104 citing papers in PubMed.
- Far from MCAR: Obtaining Population-level Estimates of HIV Viral Suppression.Epidemiology (Cambridge, Mass.) · 2020Trial
- Considerations for the Integration of Clinical Trials and Real-World Data.Clinical pharmacology and therapeutics · 2026Review
- Ten quick tips for causal analysis of biomedical omics data.PLoS computational biology · 2026Article
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- A Framework for Thinking About the Potential Public Health Impact of Epidemiologic Research.Epidemiology (Cambridge, Mass.) · 2026Article
- Powering RCTs for Marginal Effects With GLMs Using Prognostic Score Adjustment.Statistics in medicine · 2026Article
- Evaluating the basis for use of advanced stage as a surrogate endpoint for cancer mortality in screening trials: a simulation study of meta-correlation and an alternative framework.EClinicalMedicine · 2026Article
- Target trial emulation: bridging observational studies and randomized trials for health decision-making.Journal of comparative effectiveness research · 2026Article
- Causal mediation approaches for understanding pathways to inequalities and policy entry points: examples from early years health and development.Journal of epidemiology and community health · 2026Article
- When seasonal structure dominates: rethinking causal attribution in environmental epidemiology.Frontiers in epidemiology · 2026Article
- Local habitual movement as a mechanism for Schistosoma mansoni transmission resurgence: a causal analysis of cross-sectional survey data.BMJ public health · 2026Article
- A surrogate endpoint-based provisional approval causal roadmap, illustrated by vaccine development.Biostatistics (Oxford, England) · 2025Article
- Pulling back the curtain: the road from statistical estimand to machine-learning-based estimator for epidemiologists (no wizard required).American journal of epidemiology · 2025Article
- Effects Among the Affected.Statistics in medicine · 2025Article
- Illustrating an Adaptive Prespecification Framework for Observational Research: Target Trial Emulations Comparing Immunomodulator Treatments for COVID-19.Epidemiology (Cambridge, Mass.) · 2025Article
- Is Checking for Sequential Positivity Violations Getting You Down? Try sPoRT!Epidemiology (Cambridge, Mass.) · 2025Article
- Causal inference in randomized trials with partial clustering.Clinical trials (London, England) · 2025Article
- Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data.American journal of human biology : the official journal of the Human Biology Council · 2025Article
- Modeling Versus Balancing Approaches to Addressing Instrumental Variables in Weighting: A Comparison of the Outcome-Adaptive Lasso, Stable Balancing Weighting, and Stable Confounder Selection.Pharmacoepidemiology and drug safety · 2025Article
- Using Propensity Score Subclassification to Estimate the Population-Average Causal Effect of Temporomandibular Dysfunction Experience on Oral Health-Related Quality of Life Among Australian Adults.Community dentistry and oral epidemiology · 2025Article
44 more citing papers are in PubMed but not listed here.
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
The practice of epidemiology requires asking causal questions. Formal frameworks for causal inference developed over the past decades have the potential to improve the rigor of this process. However, the appropriate role for formal causal thinking in applied epidemiology remains a matter of debate. We argue that a formal causal framework can help in designing a statistical analysis that comes as close as possible to answering the motivating causal question, while making clear what assumptions are required to endow the resulting estimates with a causal interpretation. A systematic approach for the integration of causal modeling with statistical estimation is presented. We highlight some common points of confusion that occur when causal modeling techniques are applied in practice and provide a broad overview on the types of questions that a causal framework can help to address. Our aims are to argue for the utility of formal causal thinking, to clarify what causal models can and cannot do, and to provide an accessible introduction to the flexible and powerful tools provided by causal models.
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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.