Deep learning is becoming increasingly relevant in drug discovery, from de novo design to
protein structure prediction and synthesis planning. However, it is often challenged by the …

Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over
the last few years. While the technical developments are manifold, less focus has been …

In this paper, we present a novel method for the automatic classification of medical images
that learns and leverages weak causal signals in the image. Our framework consists of a …

Numerous real-world systems, ranging from healthcare to energy grids, involve users
competing for finite and potentially scarce resources. Designing policies for resource …

Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in
computer science, even though the underlying concepts of causation and explanation share …

Structural causal models (SCMs) are a powerful tool for understanding the complex causal
relationships that underlie many real-world systems. As these systems grow in size, the …

The field of machine learning and AI has witnessed remarkable breakthroughs with the
emergence of LLMs, which have also sparked a lively debate in the causal community. As …

For a given causal question, it is important to efficiently decide which causal inference
method to use for a given dataset. This is challenging because causal methods typically rely …

We present a novel technique to discover and exploit weak causal signals directly from
images via neural networks for classification purposes. This way, we model how the …

Integrating deep learning and causal discovery has increased the interpretability of
Temporal Action Segmentation (TAS) tasks. However, frame-level causal relationships exist …