Machine learning in policy evaluation: new tools for causal inference
N Kreif, K DiazOrdaz - arXiv preprint arXiv:1903.00402, 2019 - arxiv.org
While machine learning (ML) methods have received a lot of attention in recent years, these
methods are primarily for prediction. Empirical researchers conducting policy evaluations …
methods are primarily for prediction. Empirical researchers conducting policy evaluations …
[HTML][HTML] Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review
Purpose The targeted maximum likelihood estimation (TMLE) statistical data analysis
framework integrates machine learning, statistical theory, and statistical inference to provide …
framework integrates machine learning, statistical theory, and statistical inference to provide …
Targeted learning: toward a future informed by real-world evidence
Abstract The 21st Century Cures Act of 2016 includes a provision for the US Food and Drug
Administration10. 13039/100000038 (FDA) to evaluate the potential use of Real-World …
Administration10. 13039/100000038 (FDA) to evaluate the potential use of Real-World …
When can nonrandomized studies support valid inference regarding effectiveness or safety of new medical treatments?
The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects
of medications. Limitations of RCTs have led to increasing interest in using real‐world …
of medications. Limitations of RCTs have led to increasing interest in using real‐world …
Statistical modeling: the three cultures
A Daoud, D Dubhashi - arXiv preprint arXiv:2012.04570, 2020 - arxiv.org
Two decades ago, Leo Breiman identified two cultures for statistical modeling. The data
modeling culture (DMC) refers to practices aiming to conduct statistical inference on one or …
modeling culture (DMC) refers to practices aiming to conduct statistical inference on one or …
Selective machine learning of doubly robust functionals
Y Cui, EJ Tchetgen Tchetgen - Biometrika, 2024 - academic.oup.com
While model selection is a well-studied topic in parametric and nonparametric regression or
density estimation, selection of possibly high-dimensional nuisance parameters in …
density estimation, selection of possibly high-dimensional nuisance parameters in …
Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature
Purpose Supplementing investigator‐specified variables with large numbers of empirically
identified features that collectively serve as 'proxies' for unspecified or unmeasured factors …
identified features that collectively serve as 'proxies' for unspecified or unmeasured factors …
Real‐world evidence of bariatric surgery and cardiovascular benefits using electronic health records data: A lesson in bias
JA Rassen, W Murk… - Diabetes, Obesity and …, 2021 - Wiley Online Library
Aim To reproduce and correct studies on bariatric surgery and the reduction in major
adverse cardiovascular events (MACE) among patients with obesity and type 2 diabetes …
adverse cardiovascular events (MACE) among patients with obesity and type 2 diabetes …
Performance of modeling and balancing approach methods when using weights to estimate treatment effects in observational time-to-event settings
GWF Barros, M Eriksson, J Häggström - Plos one, 2023 - journals.plos.org
In observational studies weighting techniques are often used to overcome bias due to
confounding. Modeling approaches, such as inverse propensity score weighting, are …
confounding. Modeling approaches, such as inverse propensity score weighting, are …
Adaptive debiased machine learning using data-driven model selection techniques
Debiased machine learning estimators for nonparametric inference of smooth functionals of
the data-generating distribution can suffer from excessive variability and instability. For this …
the data-generating distribution can suffer from excessive variability and instability. For this …