We envision a world where policies and scientific investigations are based on verified causal relationships among quantities of interest to the question at hand. A rational understanding of such causal relationships is not only a hallmark of scientific understanding but a powerful enabler to forecasting and prediction that touches all aspects of our lives. From analyzing existing urban developments to forecasting effects of gas prices on climate change, the pursuit for causal relationships are a key ingredient of human decisions and often the ultimate goal of human investigations. This seminar aims at bringing together two complementing and mutually reinforcing approaches to understanding the causal drivers of a system: In engineering dynamical systems, described by sets of differential equations, are used to model phenomena from traffic jams to the climate, while in the social sciences, medicine, and biology, causality is analyzed in terms of probabilistic frameworks, such as the potential outcomes or causal graphical models. In each case, the resulting models are supposed to provide scientific insight into how a system would respond when subject to intervention. With a few notable exceptions, there is very little understanding of the theoretical connections or the different practical advantages of these approaches. The combination of backgrounds of the organizers aims at a workshop will bring together experts from both sides interested in causal questions. Our goal is to foster an understanding of each other’s approaches, to determine theoretical connections and generate new effective tools for teaching and applications in causality.