A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data. (arXiv:2206.04356v1 [stat.ML])

Conditional independence (CI) tests underlie many approaches to model testing
and structure learning in causal inference. Most existing CI tests for
categorical and ordinal data stratify the sample by the conditioning variables,
perform simple independence tests in each stratum, and combine the results.
Unfortunately, the statistical power of this approach degrades rapidly as the
number of conditioning variables increases. Here we propose a simple unified CI
test for ordinal and categorical data that maintains reasonable calibration and
power in high …

arxiv data ml ordinal testing