揭示潜在因果规则：用于异常事件解释的时间点过程方法
Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
AI总结：
本文提出一种基于时间点过程与EM算法的方法，自动发现“如果-那么”因果规则，用于解释医疗等高风险系统中的异常事件，并在合成与真实数据上验证了其准确性与有效性。
摘要：
在医疗保健等高风险系统中，理解异常事件（如患者健康状况的突然变化）背后的因果原因至关重要。揭示因果原因有助于快速诊断和精准治疗规划。在本文中，我们提出了一种自动化方法，用于发现解释观测事件的“如果-那么”逻辑规则。我们引入时间点过程来对感兴趣的事件进行建模，并发现一组潜在规则以解释事件的发生。为实现这一目标，我们采用了期望最大化（EM）算法。在E步中，我们计算每个事件由每条已发现规则解释的可能性。在M步中，我们更新规则集和模型参数，以增强似然函数的下界。值得注意的是，我们以微分方式优化规则集。我们的方法在发现规则和识别根本原因方面均表现出准确的性能。我们使用合成和真实医疗保健数据集展示了其有前景的结果。
英文摘要：
In high-stakes systems such as healthcare, it is critical to understand the causal reasons behind unusual events, such as sudden changes in patient's health. Unveiling the causal reasons helps with quick diagnoses and precise treatment planning. In this paper, we propose an automated method for uncovering "if-then" logic rules to explain observational events. We introduce temporal point processes to model the events of interest, and discover the set of latent rules to explain the occurrence of events. To achieve this, we employ an Expectation-Maximization (EM) algorithm. In the E-step, we calculate the likelihood of each event being explained by each discovered rule. In the M-step, we update both the rule set and model parameters to enhance the likelihood function's lower bound. Notably, we optimize the rule set in a differential manner. Our approach demonstrates accurate performance in both discovering rules and identifying root causes. We showcase its promising results using synthetic and real healthcare datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
