Causal inference via Kernel deviance measures

Conference item

Discovering the causal structure among a set of variables is a fundamental problem in many areas of science. In this paper, we propose Kernel Conditional Deviance for Causal Inference (KCDC) a fully nonparametric causal discovery method based on purely observational data. From a novel interpretation of the notion of asymmetry between cause and effect, we derive a corresponding asymmetry measure using the framework of reproducing kernel Hilbert spaces. Based on this, we propose three decision rules for causal discovery. We demonstrate the wide applicability and robustness of our method across a range of diverse synthetic datasets. Furthermore, we test our method on real-world time series data and the real-world benchmark dataset Tübingen Cause-Effect Pairs where we outperform state-of-the-art approaches.

Authors: Mitrovic, J; Sejdinovic, D; Teh, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Massachusetts Institute of Technology Press
• Host title: Advances in Neural Information Processing Systems
• Journal: Advances in Neural Information Processing Systems
• Publication date: 2018-12-31
• Acceptance date: 2018-09-05
• ISSN: 1049-5258