The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental ...

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 ...

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 ...

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...

With the emergence of huge amounts of heterogeneous multi-modal data, including images, videos, texts/languages, audios, and multi-sensor data, deep learning-based methods have shown promising ...

The Cyber Defense Review, Vol. 6, No. 4, Responding to Proxy Cyber Operations Under International Law (FALL 2021), pp. 95-115 (21 pages) In the field of Artificial Intelligence (AI), Machine Learning ...

Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...