ArticleAmerican journal of epidemiology2023
Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.
Article in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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
59 citing papers in PubMed, 15 citations in OpenAlex.
- Use of Machine Learning to Estimate the Per-Protocol Effect of Low-Dose Aspirin on Pregnancy Outcomes: A Secondary Analysis of a Randomized Clinical Trial.JAMA network open · 2022Trial
- Analyses of child cardiometabolic phenotype following assisted reproductive technologies using a pragmatic trial emulation approach.Nature communications · 2021Trial
- Overlap-Weight Estimators With Machine-Learned Plug-Ins and Overlap-Weight Targeted Maximum Likelihood Estimator.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Contributions of preeclampsia to preterm delivery: a hypothetical interventional cohort study using incremental propensity scores.International journal of epidemiology · 2026Article
- Comparing Care Coordination and Food Assistance Referrals to Improve Diabetes Outcomes: A Cohort Study.Journal of general internal medicine · 2026Article
- A review and evaluation of doubly robust approaches for estimating average treatment effects.Behavior research methods · 2026Review
- Machine learning versus logistic regression for propensity score estimation: a trial emulation benchmarked against the PARADIGM-HF randomized trial.European journal of epidemiology · 2026Article
- Oral Nicotine Product Use and E-Cigarette Use Persistence in Adolescents and Young Adults: An Analysis Using Augmented Inverse Probability Weighting.American journal of epidemiology · 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
- Recanting Twins: Addressing Intermediate Confounding in Mediation Analysis.Statistics in medicine · 2026Article
- A causal framework for evaluating drivers of policy effect heterogeneity using difference-in-differences.Health services & outcomes research methodology · 2026Article
- Environmental Chemicals as Modifiers of the Association between Age and Ovarian Reserve.medRxiv : the preprint server for health sciences · 2025Article
- Pulling back the curtain: the road from statistical estimand to machine-learning-based estimator for epidemiologists (no wizard required).American journal of epidemiology · 2025Article
- The role of cardiovascular disease as a mediator in mitigating the impact of ambient PMEnvironmental epidemiology (Philadelphia, Pa.) · 2025Article
- Covariate selection strategies and estimands - a review of current practice of risk factor analysis from a causal perspective.BMC medical research methodology · 2025Review
- Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity.Clinical and translational science · 2025Article
- Towards robust causal inference in epidemiologic research: employing double cross-fit TMLE in right heart catheterization data.American journal of epidemiology · 2025Article
- A Hypothetical PM2.5 Intervention for the Risk of Hospitalization for Cardiovascular Diseases.JAMA network open · 2025Article
- Machine Learning Versus Logistic Regression for Propensity Score Estimation: A Benchmark Trial Emulation Against the PARADIGM-HF Randomized Trial.medRxiv : the preprint server for health sciences · 2025Article
- Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.Statistics in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data generating mechanisms. As such, numerous authors have advocated for ML methods to estimate causal effects. Unfortunately, ML algorithmscan perform worse than parametric regression. We demonstrate the performance of ML-based single- and double-robust estimators. We use 100 Monte Carlo samples with sample sizes of 200, 1200, and 5000 to investigate bias and confidence interval coverage under several scenarios. In a simple confounding scenario, confounders were related to the treatment and the outcome via parametric models. In a complex confounding scenario, the simple confounders were transformed to induce complicated nonlinear relationships. In the simple scenario, when ML algorithms were used, double-robust estimators were superior to single-robust estimators. In the complex scenario, single-robust estimators with ML algorithms were at least as biased as estimators using misspecified parametric models. Double-robust estimators were less biased, but coverage was well below nominal. The use of sample splitting, inclusion of confounder interactions, reliance on a richly specified ML algorithm, and use of doubly robust estimators was the only explored approach that yielded negligible bias and nominal coverage. Our results suggest that ML based singly robust methods should be avoided.
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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.