It is well known that correlation does not imply causation. However, often the main interest lies in the impact of an intervention. Furthermore, with the increase in the amounts of data collected, issues concerning systematic bias become more and more important in modern data science. In this course, we will first define what causal effects are, and then present a reservoir of causal inference methods to estimate these effects accompanied by real-life examples.
We will also learn about basic terms as confounding and selection bias, and how to identify their potential presence and adapt our analysis plan using directed acyclic graphs