Speaker
Description
High-dimensional time series and cross-sectional data are increasingly common in finance, economics, medicine, physics, and many other fields. Building predictive models for such systems is challenging because of the large number of potential input variables. Including too many variables may lead to unnecessarily complex models, reduced predictive performance, and difficulties in interpretation.
Identifying the variables that are genuinely relevant to the underlying dynamics is therefore a crucial step in model construction. Causal methods provide a powerful framework for this task, allowing us to distinguish meaningful dependencies from spurious correlations and to identify the most relevant variables.
In this talk, I will discuss how different types of causal relationships can be uncovered directly from observational data, using methods that do not require an explicit model of the underlying system.