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 ...

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 ...

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 ...

LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.

On July 1, 2026, the new department "Computational Precision Nutrition" commenced its research activities at the German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), led by Prof. Stefan ...

Time series is data collected over time, and statistical learning is a field of statistics and machine learning that develops algorithms to model and interpret this data. Together, they use ...