Robust Event Forecasting with Spatiotemporal Confounder Learning
Robust Event Forecasting with Spatiotemporal Confounder Learning
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DOI：
10.1145/3534678.3539427
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Songgaojun Deng;H. Rangwala;Yue Ning
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文献类型：
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作者：
Songgaojun Deng;H. Rangwala;Yue Ning
数据驱动的社会事件预测方法利用相关的历史信息来预测未来事件。这些方法依赖于历史标记数据，当数据有限或质量差时，无法准确预测事件。研究事件之间的因果效应超越了相关性分析，可以有助于更可靠地预测事件。然而，由于以下几个因素，将因果关系分析纳入数据驱动的事件预测具有挑战性：（i）事件发生在复杂和动态的社会环境中。许多未观察到的变量，即，隐藏的混杂因素，影响潜在的原因和结果。(ii)给定时空非独立同分布（非IID）数据，为精确的因果效应估计建模隐藏的混杂因素并不简单。在这项工作中，我们引入了一个深度学习框架，将因果效应估计集成到事件预测中。本文首先研究了具有时空属性的观测事件数据的个体治疗效应（ITE）估计问题，提出了一种新的因果推理模型来估计ITE。然后，我们将学习到的事件相关的因果信息作为先验知识纳入事件预测。两个强大的学习模块，包括一个功能重新加权模块和近似约束损失，引入先验知识注入。我们在真实世界的事件数据集上评估了所提出的因果推理模型，并通过将学习到的因果信息输入到不同的深度学习方法中，验证了所提出的鲁棒学习模块在事件预测中的有效性。实验结果表明，建议的因果推理模型ITE估计在社会事件的优势，并展示了强大的学习模块在社会事件预测的有益特性。
Data-driven societal event forecasting methods exploit relevant historical information to predict future events. These methods rely on historical labeled data and cannot accurately predict events when data are limited or of poor quality. Studying causal effects between events goes beyond correlation analysis and can contribute to a more robust prediction of events. However, incorporating causality analysis in data-driven event forecasting is challenging due to several factors: (i) Events occur in a complex and dynamic social environment. Many unobserved variables, i.e., hidden confounders, affect both potential causes and outcomes. (ii) Given spatiotemporal non-independent and identically distributed (non-IID) data, modeling hidden confounders for accurate causal effect estimation is not trivial. In this work, we introduce a deep learning framework that integrates causal effect estimation into event forecasting. We first study the problem of Individual Treatment Effect (ITE) estimation from observational event data with spatiotemporal attributes and present a novel causal inference model to estimate ITEs. We then incorporate the learned event-related causal information into event prediction as prior knowledge. Two robust learning modules, including a feature reweighting module and an approximate constraint loss, are introduced to enable prior knowledge injection. We evaluate the proposed causal inference model on real-world event datasets and validate the effectiveness of proposed robust learning modules in event prediction by feeding learned causal information into different deep learning methods. Experimental results demonstrate the strengths of the proposed causal inference model for ITE estimation in societal events and showcase the beneficial properties of robust learning modules in societal event forecasting.