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.
翻译:有条件独立(CI)测试是许多因果推断中示范测试和结构学习方法的基础。大多数现有的绝对和正统数据的CI测试都通过调节变量对样本进行分层,在每个层进行简单的独立测试,并将结果结合起来。不幸的是,这一方法的统计能力随着调节变量数量的增加而迅速下降。我们在这里建议对保持合理校准和高维功率的常规和绝对数据进行简单统一的CI测试。我们表明,我们的测试在密集定向图形模型的模型测试和结构学习中优于现有基线,同时对稀有模型进行比较。我们的方法对因果关系模型测试具有吸引力,因为它易于实施,可以与非参数或准参数概率模型一起使用,具有对称特性,并具有合理的计算要求。