ArticleEuropean journal of epidemiology2024
Machine learning in causal inference for epidemiology.
Article in European journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review.Nursing & health sciences · 2026Article
- From association to causation: interpreting PS-based analyses in real-world evidence.Korean journal of anesthesiology · 2026Review
- Comorbidity Exposure-Window Definitions and Multidimensional Disparities in Long COVID Risk: Evidence from a U.S. National Cohort (2020-2024).medRxiv : the preprint server for health sciences · 2026Article
- Causal Inference in Clinical Research: A Primer.Journal of periodontal research · 2026Review
- Value operating system: a practical framework to measure outcomes-per-cost at the episode level.BMJ open quality · 2026Article
- When does algorithmic complexity add causal value in environmental epidemiology?Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- Machine Learning-Based Prediction of Drug-Induced QTc Changes in a Large Finnish Biobank Cohort.Clinical and translational science · 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
- Marginal causal effect of fungicide seed treatments on soybean yield and uncertain profitability in the US Midwest.Scientific reports · 2026Article
- Review
- Mapping the Analytical Landscape of Gene-Diet Interactions in Epidemiology: From Classical Models to Causal and Multi-Omics Frameworks.Nutrients · 2026Review
- Machine Learning-Based Prediction of Institutional Delivery Dropout (IDD) Among Nigerian Women: An Exploratory Study Using SHAP Interpretability.Journal of epidemiology and global health · 2026Article
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- DeepDiff-SHAP: Interpretable deep learning for subgroup-specific causal hypothesis generation using conditional SHAP.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Postoperative enteral nutrition timing and risk variation in elderly patients with perforated peptic ulcer.Frontiers in nutrition · 2026Article
- Evaluating the effectiveness and safety of Xiyanping injection for severe pneumonia: a target trial emulation protocol using real-world data.Frontiers in pharmacology · 2026Article
- Effect of sea surface temperature in El Niño regions on dengue dynamics in Colombia: Evidence from causal machine learning.PLOS global public health · 2026Article
- Global burden of cardiovascular disease attributable to lead exposure: based on the global burden of disease study 2021.Frontiers in public health · 2026Article
- Construction of a predictive model for the efficacy of enhanced external counterpulsation therapy in patients with heart failure with preserved ejection fraction based on automated machine learning and echocardiography.Frontiers in cardiovascular medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimates. Correct model specification is challenging, especially in high-dimensional settings. Incorporating Machine Learning (ML) into causal analyses may reduce the bias arising from model misspecification, since ML methods do not require the specification of a functional form of the relationship between variables. However, when ML predictions are directly plugged in a predefined formula of the effect of interest, there is the risk of introducing a "plug-in bias" in the effect measure. To overcome this problem and to achieve useful asymptotic properties, new estimators that combine the predictive potential of ML and the ability of traditional statistical methods to make inference about population parameters have been proposed. For epidemiologists interested in taking advantage of ML for causal inference investigations, we provide an overview of three estimators that represent the current state-of-art, namely Targeted Maximum Likelihood Estimation (TMLE), Augmented Inverse Probability Weighting (AIPW) and Double/Debiased Machine Learning (DML).
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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.