ReviewRoyal Society open science2022
Causal machine learning for healthcare and precision medicine.
Review in Royal Society open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 2 of them syntheses 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
68 citing papers in PubMed, 2 syntheses or guidelines pooled it, 173 citations in OpenAlex.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- The Impact of Digital Hospitals on Patient and Clinician Experience: Systematic Review and Qualitative Evidence Synthesis.Journal of medical Internet research · 2024Pooled it
- A Machine Learning Model to Predict Complications Following Head and Neck Microvascular Surgery.Laryngoscope investigative otolaryngology · 2026Article
- Towards precision medicine: Application of the structural causal model in psychiatry.General psychiatry · 2026Article
- Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.Cancer research · 2026Article
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Value operating system: a practical framework to measure outcomes-per-cost at the episode level.BMJ open quality · 2026Article
- Toward causal artificial intelligence for biologic treatment response in rheumatoid arthritis: current evidence and future directions.Rheumatology international · 2026Review
- Electronic medical record-based causal network modeling for acute myocardial infarction diagnosis in the emergency department.iScience · 2026Article
- Traditional statistics and artificial intelligence-based prognostic models for predicting type 2 diabetes mellitus after gestational diabetes: a systematic review.Diagnostic and prognostic research · 2026Review
- From data to decision: integrating causality AI and predictive analytics in endourological practice-a descriptive guide for clinicians from EAU Endourology.World journal of urology · 2026Review
- Uncovering causal relationships in single-cell omic studies with causarray.Briefings in bioinformatics · 2026Article
- Treatment Effects of Cholinesterase Inhibitors in Alzheimer's Disease: a Causal Machine Learning Approach.medRxiv : the preprint server for health sciences · 2026Article
- Fairness-aware K-means clustering in digital mental health for higher education students: a generalizable framework for equitable clustering.JAMIA open · 2026Article
- Linear Causal Discovery with Interventional Constraints.Machine learning · 2026Article
- From virtual pregnancy to digital twin obstetrics: multimodal data integration for personalized prediction of pregnancy complications.Frontiers in medicine · 2026Review
- Application of multimodal data fusion and intelligent classification in medical coding with the MCoder-T model.PloS one · 2026Article
- Artificial Intelligence to Guide Repurposing of Drugs.Annual review of medicine · 2026Review
- AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision therapy.Frontiers in oncology · 2026Review
8 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
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quantifying effects of interventions allows actionable decisions to be made while maintaining robustness in the presence of confounders. Here, we explore how causal inference can be incorporated into different aspects of clinical decision support systems by using recent advances in machine learning. Throughout this paper, we use Alzheimer's disease to create examples for illustrating how CML can be advantageous in clinical scenarios. Furthermore, we discuss important challenges present in healthcare applications such as processing high-dimensional and unstructured data, generalization to out-of-distribution samples and temporal relationships, that despite the great effort from the research community remain to be solved. Finally, we review lines of research within causal representation learning, causal discovery and causal reasoning which offer the potential towards addressing the aforementioned challenges.
Indexed as
Identifiers
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.