STAT1: Inferring Causality from Passive Observations

label Diverse autorenew 2025-09-29, 17:01 history_edu Diana Ignat
Understanding causal relations is a core problem of scientific research (e.g. effectiveness of a drug, effect of CO_2 on the climate, impact of interest rate on the market). Since causality describes the behaviour of a system under interventions it is crucial for predicting the effects of our potential actions. It is sometimes argued that causal relations can only be learned by intervening on the system. Since the 90s, however, there is an increasing number of researchers from machine learning, philosophy and statistics who believe that causal conclusions can also be derived from passive observations alone provided that appropriate assumptions are made. The course will explain the approach from the 90s inferring the causal relation between n random variables using conditional statistical dependences. Then it will describe more recent approaches to the problem that also account for statistical properties other than conditional dependences. Moreover, it will show that causal conclusions need not rely on *statistical* observations because we can also learn causal relations among single objects.

Period
18-08-2014 - 22-08-2014 (1 weeks)

Target group
The Summer School annually offers courses for advanced masters students, graduate students and post-docs in the various fields of science and information technology.

Course aim
The most important aims of the Summer School are to develop post-graduates scientific readiness and to offer students the possibility to study in a modern, scientific environment and to create connections to the international science community.



Credits
2.0 ECTS credits

Course fee
EUR 0[Convert to USD]Participating the Summer School is free of charge, but student have to cover the costs of own travel, accommodation and meals at Jyvskyl.

Course leader
Coordinator: Prof. Juha Karvanen (University of Jyvskyl, Finland)Lecturer: Dr. Dominik Janzing (Max Planck Institute for Intelligent Systems, German)

Scholarships
The 24th Jyvskyl Summer School is not able to grant any Summer School students financial support. In order to ensure your participation, we recommend that you take steps to secure your own funding, for example, by turning first to your home inst

University of Jyvaskyla Faculty of Mathematics and Science and Faculty of Information Technology
Address: Jyvaskyla Summer School, Faculty of Mathematics and Science P.O.Box 35 (YK312), FIN-40014 University of Jyvaskyla, Finland
Postal code: FIN-40014
City: Jyvaskyla
Country: Finland
Website: http://www.jyu.fi/summerschool
E-mail: jss@jyu.fi
Phone: +358505818351