Sugar-sweetened beverages (SSBs) are a major source of added sugars in the diet. A
robust body of evidence has linked habitual intake of SSBs with weight gain and a higher …

Big data, high-performance computing, and (deep) machine learning are increasingly
becoming key to precision medicine—from identifying disease risks and taking preventive …

Mendelian randomisation (MR) studies allow a better understanding of the causal effects of
modifiable exposures on health outcomes, but the published evidence is often hampered by …

Importance Many medical journals, includingJAMA, restrict the use of causal language to the
reporting of randomized clinical trials. Although well-conducted randomized clinical trials …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

In this paper, we discuss recent developments in econometrics that we view as important for
empirical researchers working on policy evaluation questions. We focus on three main …

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding
and use of data. Without an understanding of cause–effect relationships, we cannot use data …

Abstract Evaluation of treatment effects and more general estimands is typically achieved via
parametric modeling, which is unsatisfactory since model misspecification is likely. Data …

Studies consistently show associations between school absences and academic
achievement. However, questions remain about whether this link depends on the reason for …

Importance Although consumption of ultraprocessed food has been linked to higher risk of
cardiovascular disease, metabolic syndrome, and obesity, little is known about the …