The assumption that data are independent and identically distributed samples from a single underlying population is pervasive in statistical modelling. However, most data do not satisfy this assumption. Regression models have been extended to deal with structured data collected over time, spaces, and different populations. But what about causal network models, which are built on regression? In this talk, we will discuss how to produce causal models that can answer crucial causal questions in environmental sciences, epidemiology and other challenging domains that produce data with complex structures.

Marco Scutari is a Senior Researcher at Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Switzerland. He has held positions in statistics, statistical genetics and machine learning in the UK and Switzerland since completing his PhD in Statistics in 2011. His research focuses on the theory of Bayesian networks and their applications to biological and clinical data, as well as statistical computing and software engineering.