  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.
