Modeling and analysis for event series generated by heterogeneous users of various behavioral patterns are closely involved in our daily lives, including credit card fraud detection, online platform user recommendation, and social network analysis. The most commonly adopted approach to this task is to classify users into behavior-based categories and analyze each of them separately. However, this approach requires extensive data to fully understand user behavior, presenting challenges in modeling newcomers without historical knowledge. In this paper, we propose a novel discrete event prediction framework for new users through the lens of causal inference. Our method offers an unbiased prediction for new users without needing to know their categories. We treat the user event history as the ''treatment'' for future events and the user category as the key confounder. Thus, the prediction problem can be framed as counterfactual outcome estimation, with the new user model trained on an adjusted dataset where each event is re-weighted by its inverse propensity score. We demonstrate the superior performance of the proposed framework with a numerical simulation study and two real-world applications, including Netflix rating prediction and seller contact prediction for customer support at Amazon.

翻译：由具有不同行为模式的异质用户生成的事件序列建模与分析，与我们的日常生活密切相关，包括信用卡欺诈检测、在线平台用户推荐以及社交网络分析。完成此任务最常用的方法是将用户按行为进行分类，并分别分析每一类用户。然而，这种方法需要大量数据才能充分理解用户行为，这给没有历史知识的新用户建模带来了挑战。在本文中，我们通过因果推断的视角，提出了一种新颖的离散事件预测框架，专门针对新用户。我们的方法无需知晓新用户的类别，即可为其提供无偏预测。我们将用户事件历史视为对未来事件的"处理"，并将用户类别视为关键混杂因子。因此，预测问题可以构建为反事实结果估计问题，新用户模型在一个调整后的数据集上进行训练，其中每个事件都通过其逆倾向得分进行重新加权。我们通过数值模拟研究和两个实际应用（包括Netflix评分预测和亚马逊客户支持的卖家联系预测）证明了所提框架的优越性能。