论文标题
从观察数据推断因果方向：一种复杂性方法
Inferring Causal Direction from Observational Data: A Complexity Approach
论文作者
论文摘要
从观察数据中学习的因果结构的核心是一个欺骗性的简单问题：给定两个统计上依赖的随机变量，哪个变量对另一个有因果影响？仅使用统计依赖性测试，这是不可能回答的，要求我们做出其他假设。我们提出了几个快速和简单的标准，以在成对的离散或连续随机变量对中区分因果关系。它们背后的直觉是，使用CASION变量预测效应变量应该比反向的“简单” - “简单性”的不同概念产生不同的标准。我们证明了在广泛的因果机制和噪声类型下生成的合成数据标准的准确性。
At the heart of causal structure learning from observational data lies a deceivingly simple question: given two statistically dependent random variables, which one has a causal effect on the other? This is impossible to answer using statistical dependence testing alone and requires that we make additional assumptions. We propose several fast and simple criteria for distinguishing cause and effect in pairs of discrete or continuous random variables. The intuition behind them is that predicting the effect variable using the cause variable should be `simpler' than the reverse -- different notions of `simplicity' giving rise to different criteria. We demonstrate the accuracy of the criteria on synthetic data generated under a broad family of causal mechanisms and types of noise.
