Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms. In this study, we introduce LCIT (Latent representation based Conditional Independence Test)-a novel non-parametric method for conditional independence testing based on representation learning. Our main contribution involves proposing a generative framework in which to test for the independence between X and Y given Z, we first learn to infer the latent representations of target variables X and Y that contain no information about the conditioning variable Z. The latent variables are then investigated for any significant remaining dependencies, which can be performed using the conventional partial correlation test. The empirical evaluations show that LCIT outperforms several state-of-the-art baselines consistently under different evaluation metrics, and is able to adapt really well to both non-linear and high-dimensional settings on a diverse collection of synthetic and real data sets.

翻译：检测有条件的不依赖性在若干统计和机器学习任务中,特别是在因果发现算法中,起着关键作用。在本研究中,我们引入了LCIT(基于远程代表的有条件独立测试)——一种基于代表性学习的有条件独立测试的新颖的非参数方法。我们的主要贡献是提出一个基因化框架,用于测试X和Y之间的独立性。我们首先学会推断目标变量X和Y的潜在表现,这些变量没有包含关于调节变量Z的信息。然后对任何剩余的重大依赖性进行调查,这些潜在变量可以使用传统的部分相关测试进行。经验评估显示,LCIT在不同的评价指标下持续地超越了几个最先进的基线,并且能够在多种合成和真实数据集的收集中真正地适应非线性和高维度环境。