Machine learning with big data: Challenges and approaches
The Big Data revolution promises to transform how we live, work, and think by enabling
process optimization, empowering insight discovery and improving decision making. The …
process optimization, empowering insight discovery and improving decision making. The …
A biologist's guide to model selection and causal inference
A goal of many research programmes in biology is to extract meaningful insights from large,
complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with …
complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with …
Using big data to emulate a target trial when a randomized trial is not available
Ideally, questions about comparative effectiveness or safety would be answered using an
appropriately designed and conducted randomized experiment. When we cannot conduct a …
appropriately designed and conducted randomized experiment. When we cannot conduct a …
Selection bias due to loss to follow up in cohort studies
Selection bias due to loss to follow up represents a threat to the internal validity of estimates
derived from cohort studies. Over the past 15 years, stratification-based techniques as well …
derived from cohort studies. Over the past 15 years, stratification-based techniques as well …
Race, ethnicity, income concentration and 10-year change in urban greenness in the United States
Background: Cross-sectional studies suggest urban greenness is unequally distributed by
neighborhood demographics. However, the extent to which inequalities in greenness have …
neighborhood demographics. However, the extent to which inequalities in greenness have …
Reflection on modern methods: when worlds collide—prediction, machine learning and causal inference
Causal inference requires theory and prior knowledge to structure analyses, and is not
usually thought of as an arena for the application of prediction modelling. However …
usually thought of as an arena for the application of prediction modelling. However …
Causal models adjusting for time-varying confounding—a systematic review of the literature
Background Obtaining unbiased causal estimates from longitudinal observational data can
be difficult due to exposure-affected time-varying confounding. The past decade has seen …
be difficult due to exposure-affected time-varying confounding. The past decade has seen …
Greenness and birth outcomes in a range of Pennsylvania communities
Living in communities with more vegetation during pregnancy has been associated with
higher birth weights, but fewer studies have evaluated other birth outcomes, and only one …
higher birth weights, but fewer studies have evaluated other birth outcomes, and only one …
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 …
Air pollution and cardiovascular and thromboembolic events in older adults with high-risk conditions
RC Nethery, K Josey, P Gandhi, JH Kim… - American journal of …, 2023 - academic.oup.com
Little epidemiologic research has focused on pollution-related risks in medically vulnerable
or marginalized groups. Using a nationwide 50% random sample of 2008–2016 Medicare …
or marginalized groups. Using a nationwide 50% random sample of 2008–2016 Medicare …