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 algor...
Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood esti...
The consistency of propensity score (PS) estimators relies on correct specification of the PS model. The PS is frequently estimated using main-effects logistic regression. However, the underlying model assumptions may not hold. Machine learning metho...
Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses challenges for large-scale research. In this study, we developed and evaluated natural language process...
There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and met...
Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorithms, that has been used to detect and identify objects in text, audio, images, and video. Serghiou a...
When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies or the opening or closing of an industrial facility, careful statistical analysis is needed to separate causal changes from o...
In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage low...
Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, J...
Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning f...