Causal Inference via Kernel Deviance Measures
This addresses the fundamental problem of causal structure discovery for scientists across various fields, representing a novel methodological advancement rather than an incremental improvement.
The authors tackled the problem of causal discovery from observational data by proposing KCDC, a fully nonparametric method based on kernel deviance measures, which outperformed existing state-of-the-art methods on the Tubingen Cause-Effect Pairs benchmark.
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 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 Tubingen Cause-Effect Pairs where we outperform existing state-of-the-art methods.