论文标题
关系依赖的非参数推断
Non-Parametric Inference of Relational Dependence
论文作者
论文摘要
独立测试在观察数据中的统计和因果推断中起着核心作用。标准独立测试假定数据样本是独立的，并且分布相同（i.i.d。），但是在以关系系统为中心的许多现实世界数据集和应用中都违反了该假设。这项工作研究了通过定义影响个人实例的一组观察值的足够表示，从关系系统中估算独立性的问题。具体而言，我们通过将内核平均嵌入为关系变量的灵活聚合函数来定义关系数据的边际和条件独立性测试。我们提出了一个一致的，非参数，可扩展的内核测试，以对非I.I.D的关系独立性测试进行操作。一组结构假设下的观察数据。我们对各种合成和半合成网络进行了经验评估我们提出的方法，并证明了与基于最新内核的独立性测试相比其有效性。
Independence testing plays a central role in statistical and causal inference from observational data. Standard independence tests assume that the data samples are independent and identically distributed (i.i.d.) but that assumption is violated in many real-world datasets and applications centered on relational systems. This work examines the problem of estimating independence in data drawn from relational systems by defining sufficient representations for the sets of observations influencing individual instances. Specifically, we define marginal and conditional independence tests for relational data by considering the kernel mean embedding as a flexible aggregation function for relational variables. We propose a consistent, non-parametric, scalable kernel test to operationalize the relational independence test for non-i.i.d. observational data under a set of structural assumptions. We empirically evaluate our proposed method on a variety of synthetic and semi-synthetic networks and demonstrate its effectiveness compared to state-of-the-art kernel-based independence tests.
