We propose a new conditional dependence measure and a statistical test for conditional independence. The measure is based on the difference between analytic kernel embeddings of two well-suited distributions evaluated at a finite set of locations. We obtain its asymptotic distribution under the null hypothesis of conditional independence and design a consistent statistical test from it. We conduct a series of experiments showing that our new test outperforms state-of-the-art methods both in terms of type-I and type-II errors even in the high dimensional setting.
翻译:我们提出新的有条件依赖度和有条件独立统计测试。该计量基于在一定地点评估的两种合适分布的分析和内核嵌入的差别。我们在有条件独立假设的无效假设下获得无症状分布,并从中设计出一致的统计测试。我们进行了一系列实验,表明我们新的测试方法在第一类和第二类错误方面都优于最先进的方法,即使在高维环境中也是如此。