The Barcelona GSE Data Science Center coordinates and promotes interdisciplinary and methodological research, training, and knowledge transfer in Data Science.
Upcoming events in March:
Bayes Comp is a biennial conference sponsored by the ISBA section of the same name. The conference and the section both aim to promote original research into computational methods for inference and decision making and to encourage the use of frontier computational tools among practitioners, the development of adapted software, languages, platforms, and dedicated machines, and to translate and disseminate methods developed in other disciplines among statisticians. Bayes Comp is the current incarnation of the popular MCMSki series of conferences, and Bayes Comp 2018 is the first edition of this new conference series.
In the field of causality we want to understand how a system reacts under interventions. These questions go beyond statistical dependences and can therefore not be answered by standard regression or classification techniques. In this tutorial you will be introduced to the interesting problem of causal inference as well as recent developments in the field. We will introduce structural causal models, formalize interventional distributions, and define causal effects as well as show how to compute them. We will present three ideas that can be used to infer causal structure from data: (1) finding (conditional) independences in the data, (2) restricting structural equation models and (3) exploiting the fact that causal models remain invariant in different environments. If time allows, we will also show how causal concepts could be used in more classical machine learning problems. No prior knowledge about causality is required. The material is also covered in a recently published book (open access).