论文标题
在数据不足下，基于强大约束因果发现的经验贝叶斯方法
Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data
论文作者
论文摘要
因果发现是学习观察数据的变量之间的因果关系，对于许多应用程序很重要。现有的因果发现方法假设数据足够，在许多现实世界数据集中可能并非如此。结果，在有限的数据下，许多现有的因果发现方法可能会失败。在这项工作中，我们提出了贝叶斯扬声器的频繁独立性测试，以提高数据下基于约束的因果发现方法的性能：1）我们首先基于我们提出的贝叶斯方法来估计互惠信息（MI），我们提出了一项基于强大的MI独立测试； 2）其次，我们考虑了贝叶斯对假设可能性的估计，并将其纳入定义明确的统计检验中，从而进行了基于统计检验的强大独立性检验。我们将提出的独立测试应用于基于约束的因果发现方法，并评估样本不足的基准数据集上的性能。实验在SOTA方法的准确性和效率方面表现出显着的性能提高。
Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications. Existing causal discovery methods assume data sufficiency, which may not be the case in many real world datasets. As a result, many existing causal discovery methods can fail under limited data. In this work, we propose Bayesian-augmented frequentist independence tests to improve the performance of constraint-based causal discovery methods under insufficient data: 1) We firstly introduce a Bayesian method to estimate mutual information (MI), based on which we propose a robust MI based independence test; 2) Secondly, we consider the Bayesian estimation of hypothesis likelihood and incorporate it into a well-defined statistical test, resulting in a robust statistical testing based independence test. We apply proposed independence tests to constraint-based causal discovery methods and evaluate the performance on benchmark datasets with insufficient samples. Experiments show significant performance improvement in terms of both accuracy and efficiency over SOTA methods.
