提出新方法捕捉外部干预下的事件因果关系变化，提升时序推理准确性。
Uncovering Causal Relation Shifts in Event Sequences under Out-of-Domain Interventions
- 基于Transformer构建模型，融合长程依赖与外部干预信息
- 在真实与模拟数据上显著优于基线模型的因果效应估计
- 适合关注动态系统中外部扰动影响的研究者
在医疗、制造、交通等领域，推断时序事件对间的因果关系具有重要意义。现有方法多局限于特定域内的事件类型，未考虑外部域干预的影响。现实中，这类干预会显著改变因果动态。为此，我们提出一种新的因果框架，扩展经典Rubin因果框架中的平均处理效应（ATE）定义，以捕捉外部干预下时序过程中事件间因果关系的变化。设计了无偏的ATE估计器，并构建基于Transformer的神经网络模型，同时处理长程时序依赖与局部模式，将外部干预信息融入过程建模。在模拟与真实数据集上的大量实验表明，该方法在外部干预增强点过程下的ATE估计与拟合优度方面均优于基线模型。
原文摘要 · Abstract (English)
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.
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