Causal inference is important in medical research to help determine if treatments are beneficial and if natural exposures are harmful. In many settings, data collection makes causal inference ...

Although it is the goal of most statistical investigation, causal inference has traditionally been ignored by statistical theory. Fortunately, there is now intense activity in a number of fields, ...

This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...

In a perspective published in Psychoradiology, researchers from Shanghai Jiao Tong University confronted causal inference in clinical neuroscience research and advocate for more clarity and ...

In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...

Bayesian networks are probabilistic graphical models that encode conditional dependencies among variables within a directed acyclic graph. In the context of causal inference, these networks provide a ...

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...

Amblyopia is a common visual disorder that causes significant vision problems globally. Most non-ocular risk factors for amblyopia are closely related to the intrauterine environment, and are strongly ...

Which of temperature or food is more important for the richness of deep-sea animals? Dr Moriaki YASUHARA from the School of Biological Sciences, the Research Division for Ecology & Biodiversity, and ...

A new study links increased plastic waste imports to measurable increases in PM2.5 near waste disposal sites in Indonesia.