ArticleAmerican journal of epidemiology2021
Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!).
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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
11 citing papers in PubMed.
- Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.BMC medical informatics and decision making · 2025Article
- Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.American journal of epidemiology · 2025Article
- Computational Approaches for Connecting Maternal Stress to Preterm Birth.Clinics in perinatology · 2024Review
- Potential of artificial intelligence in injury prevention research and practice.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2024Article
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table.International journal of environmental research and public health · 2023Article
- Prospective predictors of electronic nicotine delivery system initiation in tobacco naive young adults: A machine learning approach.Preventive medicine reports · 2023Article
- A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda.Journal of the Indian Institute of Science · 2023Review
- Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data.Current environmental health reports · 2023Review
- Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action.International journal of environmental research and public health · 2023Article
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3 authors.
Funding
Abstract
Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspecification. Using a question-and-answer format, we provide an introduction and orientation for epidemiologists interested in using machine learning but concerned about potential bias or loss of rigor due to use of "black box" models. We conclude with sample software code that may lower the barrier to entry to using these techniques.
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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.