论文标题
一种简单的统一方法，用于测试分类和序数数据的高维条件独立性
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data
论文作者
论文摘要
有条件的独立性（CI）测试是许多因果推理中模型测试和结构学习的方法。大多数现有的CI测试用于分类和序数数据，将样品通过条件变量分类，在每个层中进行简单的独立性测试，然后结合结果。不幸的是，随着条件变量的数量增加，该方法的统计能力迅速降低。在这里，我们为序数和分类数据提出了一个简单的统一CI测试，该测试在高维度中保持合理的校准和功率。我们表明，在密集的有向图形模型的模型测试和结构学习中，我们的测试优于现有基线，同时与稀疏模型相当。我们的方法对于因果模型测试可能具有吸引力，因为它易于实现，可以与非参数或参数概率模型一起使用，具有对称属性，并且具有合理的计算要求。
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.
