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Invited Talk
Workshop: Time Series Workshop

Dominik Janzing: Quantifying causal influence in time series and beyond

Dominik Janzing


Quantification of causal influence is a non-trivial conceptual problem. Well-known concepts like Granger causality and transfer entropy are arguably correct to detect the presence of causal influence (subject to assumptions like causal sufficiency and positive probability density), but following [2] I argue that taking them as measure for the strength of causal influence is conceptually flawed. To discuss this, I consider the more general question of quantifying the strength of an edge (or a set of edges) in a causal DAG. I describe a few postulates that we [1] would expect from a measure of causal influence and describe the information theoretic casual strength that we proposed in [1]. Reference: [1] D. Janzing, D. Balduzzi, M. Grosse-Wentrup, B. Schölkopf: Quantifying causal influences. Annals of Statistics, 2013. [2] N. Ay and D. Polani: Information flow in causal networks, 2008.