Invariant Causal Prediction
Seminar by Jonas Peters from University of Copenhagen
Tuesday 20 February 2018 (12h15 - 13h15) - Internef - 237
Why are we interested in the causal structure of a process? In classical prediction tasks as regression, for example, it seems that no causal knowledge is required. In many situations, however, we want to understand how a system reacts under interventions, e.g., in gene knock-out experiments. Here, causal models become important because they are usually considered invariant under those changes. A causal prediction uses only direct causes of the target variable as predictors; it remains valid even if we intervene on predictor variables or change the whole experimental setting. In this talk, we show how we can exploit this invariance principle to estimate causal structure from data. We apply the methodology to data sets from biology, epidemiology, and finance.
The talk does not require any knowledge about causal concepts.
Jonas is an associate professor in statistics at the University of Copenhagen. Previously, Jonas has been a group leader at the MPI for Intelligent Systems in Tuebingen and a Marie Curie fellow at the Seminar for Statistics, ETH Zurich. He studied Mathematics at the University of Heidelberg and the University of Cambridge and did his PhD with B. Schoelkopf, D. Janzing and P. Buehlmann. He is interested in inferring causal relationships from observational data and works both on theory, methodology, and applications. His work relates to areas as computational statistics, graphical models, independence testing or high-dimensional statistics.
Department of Information Systems