ReviewFrontiers in medicine2021
Big Data, Data Science, and Causal Inference: A Primer for Clinicians.
Review in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration.Kidney360 · 2026Review
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular medicine · 2026Article
- Prognostic evaluation of boys with posterior urethral valves using a recursive feature elimination machine learning framework.Frontiers in pediatrics · 2026Article
- Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.Journal of medical systems · 2024Review
- Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.Current HIV/AIDS reports · 2024Review
- Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives.Biomedicines · 2024Review
- Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.The Journal of allergy and clinical immunology · 2024Article
- Application of Asthma Prediction Tools in a Cohort of Infants with Severe Bronchiolitis.Pediatric allergy, immunology, and pulmonology · 2023Article
- Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation.Diagnostics (Basel, Switzerland) · 2023Article
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- Optimizing Maternal Nutrition: The Importance of a Tailored Approach.Current developments in nutrition · 2022Review
- Predictive models for clinical decision making: Deep dives in practical machine learning.Journal of internal medicine · 2022Review
- Role of nasal microbiota and host response in infants with respiratory syncytial virus infection: Causal questions about respiratory outcomes.The Journal of allergy and clinical immunology · 2022Article
- Toward a causal model of chronic back pain: Challenges and opportunities.Frontiers in computational neuroscience · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Clinicians handle a growing amount of clinical, biometric, and biomarker data. In this "big data" era, there is an emerging faith that the answer to all clinical and scientific questions reside in "big data" and that data will transform medicine into precision medicine. However, data by themselves are useless. It is the algorithms encoding causal reasoning and domain (e.g., clinical and biological) knowledge that prove transformative. The recent introduction of (health) data science presents an opportunity to re-think this data-centric view. For example, while precision medicine seeks to provide the right prevention and treatment strategy to the right patients at the right time, its realization cannot be achieved by algorithms that operate exclusively in data-driven prediction modes, as do most machine learning algorithms. Better understanding of data science and its tasks is vital to interpret findings and translate new discoveries into clinical practice. In this review, we first discuss the principles and major tasks of data science by organizing it into three defining tasks: (1) association and prediction, (2) intervention, and (3) counterfactual causal inference. Second, we review commonly-used data science tools with examples in the medical literature. Lastly, we outline current challenges and future directions in the fields of medicine, elaborating on how data science can enhance clinical effectiveness and inform medical practice. As machine learning algorithms become ubiquitous tools to handle quantitatively "big data," their integration with causal reasoning and domain knowledge is instrumental to qualitatively transform medicine, which will, in turn, improve health outcomes of patients.
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What OpenQuestion holds
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.