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.

翻译：在高风险系统（如医疗健康领域）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确治疗规划。本文提出一种自动化方法，用于挖掘"如果-那么"逻辑规则以解释观测事件。我们引入时间点过程对目标事件进行建模，并发现解释事件发生的潜在规则集合。为此，我们采用期望最大化（EM）算法：在E步中，计算每个事件被每条已发现规则解释的似然值；在M步中，同时更新规则集和模型参数以提升似然函数的下界。值得注意的是，我们以可微方式优化规则集。该方法在规则发现与根本原因识别方面均展现出准确性能。我们通过合成数据集和真实医疗数据集验证了其卓越效果。