A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data
This addresses a bottleneck in causal inference for researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackles the problem of statistical power degradation in conditional independence tests for categorical and ordinal data as the number of conditioning variables increases, proposing a simple unified test that outperforms existing baselines in dense directed graphical models while being comparable for sparse models.
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 dimensions. We show that our test outperforms existing baselines in model testing and structure learning for dense directed graphical models while being comparable for sparse models. Our approach could be attractive for causal model testing because it is easy to implement, can be used with non-parametric or parametric probability models, has the symmetry property, and has reasonable computational requirements.