论文标题
因果方向推断的平行合奏方法
Parallel ensemble methods for causal direction inference
论文作者
论文摘要
从它们的观察数据中推断两个变量之间的因果方向是数据科学中最基本和最具挑战性的主题之一。因果方向推断算法将观察数据映射到代表x导致y或y的二进制值引起x。这些算法的性质使结果随数据点的变化而不稳定。因此，可以使用平行的集合框架可以显着提高因果方向推断的准确性。在本文中，提出了基于几种平行集合方式的新因果方向推论算法。对精度率进行了理论分析。实验均在两个人工数据集和现实世界数据集上进行。证明了该方法及其在平行计算环境中的计算效率的准确性性能。
Inferring the causal direction between two variables from their observation data is one of the most fundamental and challenging topics in data science. A causal direction inference algorithm maps the observation data into a binary value which represents either x causes y or y causes x. The nature of these algorithms makes the results unstable with the change of data points. Therefore the accuracy of the causal direction inference can be improved significantly by using parallel ensemble frameworks. In this paper, new causal direction inference algorithms based on several ways of parallel ensemble are proposed. Theoretical analyses on accuracy rates are given. Experiments are done on both of the artificial data sets and the real world data sets. The accuracy performances of the methods and their computational efficiencies in parallel computing environment are demonstrated.
