In the past decade, machine learning has enjoyed considerable succes in a wide variety of applications and domains. However, more and more it has become clear that some key elements are missing in the current approaches, such as a causal structure and understanding of the world. On the other side of the spectrum, the field of causal inference has long evolved independently from the big data and AI revolutions. In recent years, there has been considerable progress in combining causal inference and machine learning, to the advantage of both fields. In this seminar we will briefly introduce the main concepts and challenges in the two domains and the most important ways in which they can benefit from each other.