ArticleAmerican journal of epidemiology2021
Demystifying Statistical Inference When Using Machine Learning in Causal Research.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
What it found
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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.
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Who cites it
22 citing papers in PubMed.
- Comparing Care Coordination and Food Assistance Referrals to Improve Diabetes Outcomes: A Cohort Study.Journal of general internal medicine · 2026Article
- Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning.Clinical trials (London, England) · 2026Article
- REFINE2: a simplified simulation tool to help epidemiologists evaluate the suitability and sensitivity of effect estimation within user-specified data.American journal of epidemiology · 2026Article
- A causal framework for evaluating drivers of policy effect heterogeneity using difference-in-differences.Health services & outcomes research methodology · 2026Article
- Pulling back the curtain: the road from statistical estimand to machine-learning-based estimator for epidemiologists (no wizard required).American journal of epidemiology · 2025Article
- Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be.Epidemiology (Cambridge, Mass.) · 2025Article
- Towards robust causal inference in epidemiologic research: employing double cross-fit TMLE in right heart catheterization data.American journal of epidemiology · 2025Article
- Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.Statistics in medicine · 2025Article
- Performance of Cross-Validated Targeted Maximum Likelihood Estimation.Statistics in medicine · 2025Article
- High-Dimensional Disease Risk Score for Dealing With Residual Confounding Bias in Estimating Treatment Effects With a Survival Outcome.Pharmacoepidemiology and drug safety · 2025Article
- Practical Considerations for Variable Screening in the Super Learner.The New England Journal of Statistics in Data Science · 2025Article
- How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High-Dimensional Proxies to Reduce Residual Confounding?Pharmacoepidemiology and drug safety · 2025Article
- Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.Statistics in medicine · 2025Article
- Prognostic score-based model averaging approach for propensity score estimation.BMC medical research methodology · 2024Article
- Machine learning in causal inference for epidemiology.European journal of epidemiology · 2024Article
- Handling missing data when estimating causal effects with targeted maximum likelihood estimation.American journal of epidemiology · 2024Article
- Implementing TMLE in the presence of a continuous outcome.Research methods in medicine & health sciences · 2024Article
- Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology.BMC medical research methodology · 2023Article
- Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research.iScience · 2023Article
- CORRECTION TO: "INVITED COMMENTARY: DEMYSTIFYING STATISTICAL INFERENCE WHEN USING MACHINE LEARNING IN CAUSAL RESEARCH".American journal of epidemiology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this issue, Naimi et al. (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) discuss a critical topic in public health and beyond: obtaining valid statistical inference when using machine learning in causal research. In doing so, the authors review recent prominent methodological work and recommend: (i) double robust estimators, such as targeted maximum likelihood estimation (TMLE); (ii) ensemble methods, such as Super Learner, to combine predictions from a diverse library of algorithms, and (iii) sample-splitting to reduce bias and improve inference. We largely agree with these recommendations. In this commentary, we highlight the critical importance of the Super Learner library. Specifically, in both simulation settings considered by the authors, we demonstrate that low bias and valid statistical inference can be achieved using TMLE without sample-splitting and with a Super Learner library that excludes tree-based methods but includes regression splines. Whether extremely data-adaptive algorithms and sample-splitting are needed depends on the specific problem and should be informed by simulations reflecting the specific application. More research is needed on practical recommendations for selecting among these options in common situations arising in epidemiology.
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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.