论文标题
原因：使用归因方法从事件序列中学习Granger因果关系
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
论文作者
论文摘要
我们研究了从异步，相互依存的多类事件序列学习事件类型之间的Granger因果关系的问题。现有工作遭受有限的模型灵活性或差的模型解释性，因此无法揭示各种事件序列中具有不同事件相互依存的各种事件序列的Granger因果关系。为了解决这些弱点，我们提出了原因（因事件序列的归因而有因果关系），这是研究任务的新框架。原因的关键思想是首先通过拟合神经点过程隐式捕获基本事件相互依赖性，然后使用公理归因方法从该过程中提取Granger因果关系统计量。在多个具有不同事件相互依存关系的数据集中，我们证明了在正确推断型型Granger因果关系方面，在一系列最新方法上，可以在正确地推断出类型的Granger因果关系方面取得了出色的性能。
We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. Existing work suffers from either limited model flexibility or poor model explainability and thus fails to uncover Granger causality across a wide variety of event sequences with diverse event interdependency. To address these weaknesses, we propose CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework for the studied task. The key idea of CAUSE is to first implicitly capture the underlying event interdependency by fitting a neural point process, and then extract from the process a Granger causality statistic using an axiomatic attribution method. Across multiple datasets riddled with diverse event interdependency, we demonstrate that CAUSE achieves superior performance on correctly inferring the inter-type Granger causality over a range of state-of-the-art methods.
