Current intelligent systems mainly make "predictions". That is, given an input, they estimate the most probable value of the output. These systems have many limitations, they can be easily confused when presented with a different case from their training set and they cannot explain how they arrive to a certain result. Causal models are an alternative to extend the capabilities of current systems; explain the reasons for certain decisions, predict the effect of interventions and imagine alternative situations. In this talk, I will present an introduction to causal models, in particular to causal graphical models. We will see how we can make inferences based on these models: predictions and counterfactuals; as well as learning causal models from data. I will illustrate the application of causal models in various domains: estimation of effective connectivity in the brain, causal modeling of COVID-19, and incorporating causal models in reinforcement learning and its application in robotics. Finally, I will discuss some potential applications of causal modeling in astrophysics.