Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these patterns, they often interfered by non-periodic public events, such as disasters and occasional celebrations. Since regular human mobility patterns are heavily affected by these events, estimating their causal effects is critical to accurate mobility predictions. Although news articles provide unique perspectives on these events in an unstructured format, processing is a challenge. In this study, we propose a causality-augmented prediction model, called \textbf{CausalMob}, to analyze the causal effects of public events. We first utilize large language models (LLMs) to extract human intentions from news articles and transform them into features that act as causal treatments. Next, the model learns representations of spatio-temporal regional covariates from multiple data sources to serve as confounders for causal inference. Finally, we present a causal effect estimation framework to ensure event features remain independent of confounders during prediction. Based on large-scale real-world data, the experimental results show that the proposed model excels in human mobility prediction, outperforming state-of-the-art models.

翻译：大规模人类移动展现出时空模式，这些模式可协助决策者进行政策制定。尽管传统预测模型试图捕捉这些模式，但它们常受到非周期性公共事件（如灾害和偶发性庆典）的干扰。由于规律性的人类移动模式深受此类事件影响，准确估计其因果效应对于实现精准的移动预测至关重要。虽然新闻报道以非结构化形式提供了关于这些事件的独特视角，但对其进行处理是一项挑战。在本研究中，我们提出了一种名为 **CausalMob** 的因果增强预测模型，用于分析公共事件的因果效应。我们首先利用大语言模型从新闻报道中提取人类意图，并将其转化为作为因果干预的特征。接着，该模型从多源数据中学习时空区域协变量的表征，以作为因果推断的混杂因子。最后，我们提出了一个因果效应估计框架，以确保在预测过程中事件特征与混杂因子保持独立。基于大规模真实世界数据的实验结果表明，所提模型在人类移动预测方面表现卓越，其性能优于现有最先进的模型。