ReviewNature medicine2024
Causal machine learning for predicting treatment outcomes.
Review in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 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
123 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Pooled it
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial.Nature medicine · 2025Trial
- Identification of individuals who benefit from omega-3 fatty acid supplementation to prevent coronary heart disease: a machine-learning analysis of the VITAL.European journal of epidemiology · 2025Trial
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- 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
- Machine learning methods for estimating personalized treatment effects-insights on validity from two large trials.American journal of epidemiology · 2026Article
- The beneficial relation between soil organic carbon and maize yield in field trials does not translate to real-world farms in Northeast China.Nature food · 2026Article
- Individualised treatment effects of corticosteroids in IgA nephropathy.EBioMedicine · 2026Article
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- PET/CT-derived whole-body composition and survival in resectable NSCLC: double machine learning-based adjusted association analysis of intermuscular adiposity burden and sex-specific metabolic phenotypes.European journal of nuclear medicine and molecular imaging · 2026Article
- Explaining building damage from wildfires in California.Science advances · 2026Article
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
- Empowering clinical trial design with agentic intelligence and real-world data.Nature communications · 2026Article
- Review
- Consistent analgesic effect of intravenous dexamethasone on rebound pain after brachial plexus block: a causal machine learning approach.The Korean journal of pain · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Artificial intelligence in congenital heart surgery: a scoping review and primer for surgeons.Translational pediatrics · 2026Review
- Causal inference and digital twins: a roadmap for the future of clinical trials.NPJ digital medicine · 2026Review
- Article
63 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
10 authors.
Funding
Abstract
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.
Indexed as
Identifiers
38641741What Socratic 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 Socratic graph.