Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
This work addresses the need for causal explanations of unusual events in healthcare, which can aid in diagnoses and treatment, but it appears incremental as it builds on existing temporal point process and EM techniques.
The paper tackles the problem of explaining abnormal events in high-stakes systems like healthcare by proposing an automated method to uncover latent causal 'if-then' rules using temporal point processes and an EM algorithm, demonstrating accurate performance in rule discovery and root cause identification on synthetic and real datasets.
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.