Since the very beginning of science and philosophy, causality has been a basic category of research. In the theory of dynamical systems, different forms of causality can be distinguished depending on different equations of motion. The question arises how causal relationships can be inferred from observational data. Statistic data analysis often yields information on correlations only, but not on causation. Under special conditions probabilistic distributions of data are connected with causal networks. Causal modeling plays an eminent role in the natural sciences (e.g. physics, chemistry, biology). In engineering sciences, causal dependence must not only be recognized, but constructed and controlled, in order to guarantee reliable and desired functions of technical systems. Control is the inverse problem of causality for engineers. In social sciences, causal networks are used to analyze social and economic interactions in, for example, markets, organizations, and institutions. With respect to volatility shocks and financial crashes, it is a challenge to discover the causes of extreme events. From an epistemic and interdisciplinary point of view, complex nonlinear causal networks are distinguished by universal properties, which are true in natural, technical, and social networks (e.g. scale-invariance, power laws).