Conditional independence testing: a predictive perspective
This work addresses conditional independence testing, a key tool for evaluating feature usefulness in supervised prediction, with incremental improvements in power for specific applications.
The authors tackled the problem of conditional independence testing in a predictive context by proposing a novel test based on permutation and comparing predictive power, achieving better power than competing approaches in several settings.
Conditional independence testing is a key problem required by many machine learning and statistics tools. In particular, it is one way of evaluating the usefulness of some features on a supervised prediction problem. We propose a novel conditional independence test in a predictive setting, and show that it achieves better power than competing approaches in several settings. Our approach consists in deriving a p-value using a permutation test where the predictive power using the unpermuted dataset is compared with the predictive power of using dataset where the feature(s) of interest are permuted. We conclude that the method achives sensible results on simulated and real datasets.