 Causal modeling is a burgeoning area of research. This entry has largely ignored work on computational methods, as well as applications of the tools discussed here. Rather, the focus has been on the conceptual underpinnings of recent programs in causal modeling, with special attention to the connection between causation and probability. It has also focused on what it possible to learn about causation “in principle” on the basis of probabilities, while ignoring the practical problems of making causal inferences on the basis of finite data samples (which inevitably deviate from the true probabilities).