Observational studyHealth services research2019
Estimating treatment effects with machine learning.
Observational study in Health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- A Causal Machine Learning Framework for Estimating the Impact of Cancer Diagnosis on Receipt of Advance Care Planning.Health services research · 2026Article
- Toward precision stroke rehabilitation: an integrated causal machine learning and clinician feedback approach.JAMIA open · 2025Article
- Review
- Harnessing Artificial Intelligence in Interventional Cardiology: A Systematic Review of Current Applications.Cureus · 2025Review
- An overview of modern machine learning methods for effect measure modification analyses in high-dimensional settings.SSM - population health · 2025Article
- Do health care quality improvement policies work for all? Distributional effects by baseline quality in South Africa.Health economics · 2025Article
- Machine learning in causal inference for epidemiology.European journal of epidemiology · 2024Article
- Can supervised deep learning architecture outperform autoencoders in building propensity score models for matching?BMC medical research methodology · 2024Article
- Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity.BMC medical research methodology · 2024Article
- Comparison of Low-Value Care Among Commercial and Medicaid Enrollees.Journal of general internal medicine · 2023Article
- External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning.BMC medical research methodology · 2022Article
- Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions.Artificial intelligence in medicine · 2022Article
- Precision Reimbursement for Precision Medicine: Using Real-World Evidence to Evolve From Trial-and-Project to Track-and-Pay to Learn-and-Predict.Clinical pharmacology and therapeutics · 2022Review
- Use of behavioral health care in Medicaid managed care carve-out versus carve-in arrangements.Health services research · 2021Article
- Estimating treatment effects with machine learning.Health services research · 2019Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
objectiveTo demonstrate the performance of methodologies that include machine learning (ML) algorithms to estimate average treatment effects under the assumption of exogeneity (selection on observables). DATA SOURCES: Simulated data and observational data on hospitalized adults. STUDY
designWe assessed the performance of several ML-based estimators, including Targeted Maximum Likelihood Estimation, Bayesian Additive Regression Trees, Causal Random Forests, Double Machine Learning, and Bayesian Causal Forests, applying these methods to simulated data as well as data on the effects of right heart catheterization. PRINCIPAL
findingsIn Monte Carlo studies, ML-based estimators generated estimates with smaller bias than traditional regression approaches, demonstrating substantial (69 percent-98 percent) bias reduction in some scenarios. Bayesian Causal Forests and Double Machine Learning were top performers, although all were sensitive to high dimensional (>150) sets of covariates.
conclusionsML-based methods are promising methods for estimating treatment effects, allowing for the inclusion of many covariates and automating the search for nonlinearities and interactions among variables. We provide guidance and sample code for researchers interested in implementing these tools in their own empirical work.
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.