ArticleInternational journal of epidemiology2021
Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference.
Article in International journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- Integrating job design and cognitive-emotional processes using latent network analysis: associations with performance and emotional well-being in navy personnel.BMC psychology · 2025Article
- Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges.Current environmental health reports · 2025Review
- Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods.Statistics in medicine · 2025Review
- Unbound bilirubin and risk of severe neurodevelopmental impairment in extremely low birthweight newborns.Pediatric research · 2025Article
- Mechanisms of Change in Exposure Therapy for Anxiety and Related Disorders: A Research Agenda.Clinical psychological science : a journal of the Association for Psychological Science · 2025Article
- CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction.BMC medical informatics and decision making · 2025Article
- Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.American journal of epidemiology · 2025Article
- Comparative analysis of correlation and causality inference in water quality problems with emphasis on TDS Karkheh River in Iran.Scientific reports · 2025Article
- How to select predictive models for decision-making or causal inference.GigaScience · 2025Article
- Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Review
- Machine learning in causal inference for epidemiology.European journal of epidemiology · 2024Article
- Change in employment status and its causal effect on suicidal ideation and depressive symptoms: A marginal structural model with machine learning algorithms.Scandinavian journal of work, environment & health · 2024Article
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- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- What is circulating factor disease and how is it currently explained?Pediatric nephrology (Berlin, Germany) · 2023Review
- Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.Health economics review · 2023Article
- Evaluating the Impact and Potential Impact of Machine Learning on Medical Decision Making.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to 'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs.
Indexed as
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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.