论文标题
通过风险估计来评估因果结构学习算法
Evaluation of Causal Structure Learning Algorithms via Risk Estimation
论文作者
论文摘要
近年来，从数据中学习因果结构的方法的进步。但是，对此类方法的经验评估的发展要少得多。在这个差距的推动下，我们提出了以下问题：一个人如何在给定的问题设定中评估一种或多种因果结构学习方法的实际功效？我们通过因果环境的预期损失或风险的概念在决策理论框架中正式化了问题。我们介绍了可因果风险以及可以从数据计算的样本数量的理论概念，并通过理论上和通过广泛的模拟研究研究两者之间的关系。我们的结果提供了一个假设光框架，用于评估可在一系列实用用例中应用的因果结构学习方法。
Recent years have seen many advances in methods for causal structure learning from data. The empirical assessment of such methods, however, is much less developed. Motivated by this gap, we pose the following question: how can one assess, in a given problem setting, the practical efficacy of one or more causal structure learning methods? We formalize the problem in a decision-theoretic framework, via a notion of expected loss or risk for the causal setting. We introduce a theoretical notion of causal risk as well as sample quantities that can be computed from data, and study the relationship between the two, both theoretically and through an extensive simulation study. Our results provide an assumptions-light framework for assessing causal structure learning methods that can be applied in a range of practical use-cases.
