The questions that motivate most data analysis are not associational but causal in nature. What are the causes and what are the effects of events under observation? Nevertheless, the statistical methods commonly used today to answer those questions are of associational nature. But “Correlation does not imply causation!”, and misinterpretation often results in incorrect deduction.
In the recent years, causality has grown from a nebulous concept into a mathematical theory. This is largely due to the work of Judea Pearl (2009) who has received the 2011 Turing Award for “fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning”.
In this lecture, you look at the mathematical concepts that build the basis of causal inference and how they can be used to derive causal relationships from observational data. After you gained an overview on the needed concepts, you will work on particular tasks in the context of the causal inference procedure. Moreover, you will present your work and write a scientific report about your implementation results.
