Challenges in Causality

Google Tech Talks February, 11 2008 ABSTRACT What affects your health, the economy, climate changes? And what actions will have beneficial effects? These are some of the central questions of causal discovery. A "causal model" is a model capable of making predictions under changing circumstances, corresponding to actions of "external agents" on a system of interest. For example, a doctor administering a drug to a patient, a government enforcing a new tax law or a new environmental policy. It is often necessary to assess the benefits and risks of potential actions using available past data and excluding the possibility of experimenting. Experiments, which are the ultimate way of verifying causal relationships, are in many cases too costly, infeasible, or unethical. For instance, enforcing a law prohibiting to smoke in public places is costly, preventing people from smoking may be infeasible, and forcing them to smoke would be unethical. In contrast, "observational data" are available in abundance in many applications. Recently, methods to devise causal models from observational data have been proposed. Can causal models thus obtained be relied upon to make important decisions? In this presentation, we will challenge the hopes an promises of causal discovery and present new means of assessing the validity of causal modeling techniques. Want to play? Check the "causation and prediction" competition presently going on: http://www.causality.inf.ethz.ch/challenge.php Deadline April 30, 2008 Speaker: Isabelle Guyon Isabelle Guyon is a researcher in machine learning and an independent consultant. Prior to starting her consulting practice in 1996, she worked at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces and invented Support Vector Machines (in collaboration with B. Boser and V. Vapnik). Isabelle Guyon holds a Ph.D. degree in Physical Sciences of the University Pierre and Marie Curie of Paris, France. She is vice-president of the Unipen foundation, action editor of the Journal of Machine Learning Research, and competition chair of the IJCNN conference.