**The course is given by Maciej Liśkiewicz.**
Discovering and understanding causal relationships is an important task of empirical sciences. For example, recognising the causes of diseases or economic crises is of great social interest. However, for ethical or economic reasons questions as “Does smoking cause lung cancer?” can be difficult to examine through direct experimentation. On the other hand, there are often available large amounts of observed data that can provide relevant information about these issues. The goal of causal inference — a quickly growing sub-area of AI — is to establish cause-effect relationships combining observed data with existing knowledge. The main focus of this course is on the algorithmic issues of causality that enable broad applications of the causal theory in AI and empirical sciences.
**A tentative schedule is to have three Friday meetings (28.10, 18.11, 2.12.2022).**
