Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and
application will be balanced, with students working directly with network data throughout the course. ...
This course introduces the principles of model identification for non-linear dynamic systems, and provides a set of possible solution methods that are thoroughly characterized in terms of modelling assumptions and uncertainty levels. ...
This course will give a unified presentation of modern methods for causal inference. We focus on concepts, and we will present examples and ideas from various scientific disciplines, including medicine, computer science, engineering, economics and epidemio ...
This course covers recent methodology for causal inference in settings with time-varying exposures (longitudinal data) and causally connected units (interference). We will consider theory for identification and estimation of effects, illustrated by real-li ...
We provide a comprehensive overview of the econometric tools that are essential to estimate financial models, both for asset pricing and
for corporate finance. ...