Violent conduct in society is a major health concern, and therefore one of the major aims in
forensic mental healthcare is the assessment of the risk for (future) violence. The prediction …

Causal representation learning promises to extend causal models to hidden causal
variables from raw entangled measurements. However, most progress has focused on …

Abstract Machine Learning and AI have the potential to transform data-driven scientific
discovery, enabling accurate predictions for several scientific phenomena. As many …

Simple Summary This study looks at how combining real-world/time data/evidence
(RWTD/E) with traditional clinical studies (known as randomized controlled trials) can …

Researchers in ubiquitous computing have long promised that passive sensing will
revolutionize mental health measurement by detecting individuals in a population …

Understanding the intricacies of fine-grained population distribution, including both
predictability and uncertainty, is crucial for urban planning, social equity, and environmental …

Treatment effect estimation in continuous time is crucial for personalized medicine.
However, existing methods for this task are limited to point estimates of the potential …

Predicting potential outcomes of interventions from observational data is crucial for decision-
making in medicine, but the task is challenging due to the fundamental problem of causal …

State-of-the-art methods for conditional average treatment effect (CATE) estimation make
widespread use of representation learning. Here, the idea is to reduce the variance of the …

The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a
better future by" leaving no one behind", and, to achieve the SDGs by 2030, poor countries …