My research focuses on causality in machine learning. In particular, I am interested in discovering causal mechanisms in state-of-the-art architectures. Motivated by this, I design modules that connect interventions in the representation space to interpretable modifications in the real world. With a special focus on clinical applications, I study causal mechanisms in foundation models for medicine. This enables us to move toward a causal understanding of modern deep learning architectures.