标题： 在域外干预下揭示事件序列中的因果关系变化 显示英文标题

标题： Uncovering Causal Relation Shifts in Event Sequences under Out-of-Domain Interventions

摘要： 在时间序列中推断事件对之间的因果关系在许多领域都有应用，如医疗保健、制造和交通。 现有的大多数因果推断工作主要关注指定领域内的事件类型，而没有考虑外部跨领域干预的影响。 在现实环境中，这些跨领域干预可能会显著改变因果动态。 为了解决这一问题，我们提出了一种新的因果框架，以定义平均处理效应（ATE），超越经典Rubin因果框架中的独立同分布（i.i.d.）数据，以捕捉在跨领域干预下时间过程事件之间的因果关系变化。 我们设计了一个无偏的ATE估计器，并构建了一个基于Transformer的神经网络模型，以处理长距离的时间依赖性和局部模式，同时将跨领域干预信息整合到过程建模中。 在模拟和真实世界数据集上的大量实验表明，我们的方法在跨领域增强点过程下的ATE估计和拟合优度方面优于基线方法。 显示英文摘要

摘要： Inferring causal relationships between event pairs in a temporal sequence is applicable in many domains such as healthcare, manufacturing, and transportation. Most existing work on causal inference primarily focuses on event types within the designated domain, without considering the impact of exogenous out-of-domain interventions. In real-world settings, these out-of-domain interventions can significantly alter causal dynamics. To address this gap, we propose a new causal framework to define average treatment effect (ATE), beyond independent and identically distributed (i.i.d.) data in classic Rubin's causal framework, to capture the causal relation shift between events of temporal process under out-of-domain intervention. We design an unbiased ATE estimator, and devise a Transformer-based neural network model to handle both long-range temporal dependencies and local patterns while integrating out-of-domain intervention information into process modeling. Extensive experiments on both simulated and real-world datasets demonstrate that our method outperforms baselines in ATE estimation and goodness-of-fit under out-of-domain-augmented point processes.