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. ...
On introduit les bases de l'automatique linéaire discrète qui consiste à appliquer une commande sur des intervalles uniformément espacés. La cadence de l'échantillonnage qui est associée joue un rôle primordial pour assurer la stabilité et la performance d ...
This course consists of three parts: an introduction to financial time series data characteristics and analysis, a discussion on econometrics techniques (eg, GARCH models, cointegration, extreme values, truncation), and an exploration of machine learning t ...
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 ...
This seminar will provide a survey of the canonical literature in causal inference. At the end of this course, students will gain a broad understanding of the most important methodological concepts and tools in this field, and will be equipped to criticall ...