AI 中文总结
针对预测性流程监控（PPM）模型的黑盒问题，提出基于实际因果关系框架的局部解释算法，在真实PPM基准数据集上验证其能生成更稳定简洁的解释且效率具竞争力。
AI 中文摘要
流程挖掘被广泛用于诊断流程、识别性能与合规性问题，具体而言，预测性流程监控（Predictive Process Monitoring，PPM）技术利用AI模型预测正在运行的流程实例的结果。尽管这些模型可实现较高的预测性能，但其黑盒特性使得难以理解输出预测背后的根本原因。本文提出一种基于实际因果关系框架的新颖方法，用于生成过程监控器预测的局部（案例级）解释。我们定义了一种针对流程定制的因果模型，该模型捕获轨迹中事件之间的时间依赖关系，从而使我们能够推理事件对预测结果的因果影响。我们的方法隐式使用该模型来计算原因，并量化不同事件相对于预测结果的重要性。我们提出了一种实用的、与模型无关的算法，该算法在给定因果模型所反映的流程结构的情况下，近似计算事件的责任度。我们在一系列源自标准PPM基准的真实事件日志的数据集上评估了我们的方法，每个数据集包含多达130,000条轨迹，轨迹长度多达1,800个事件，以及多达400种不同的事件类型。我们将我们的方法与最先进的局部解释方法进行比较，结果表明，我们的方法在保持具有竞争力的效率的同时，能生成更稳定、更简洁的解释。
英文摘要
Process mining is widely used to diagnose processes and identify performance and compliance issues. Specifically, Predictive Process Monitoring (PPM) techniques use AI models to predict outcomes of ongoing process instances. While these models can achieve high predictive performance, their black-box nature makes it difficult to understand the underlying reasons behind their output predictions. In this paper, we propose a novel approach for generating local (case-level) explanations of process monitor predictions based on the framework of actual causality. We define a causal model tailored to processes that captures temporal dependencies between events in a trace, thus allowing us to reason about causal influence of events on the predicted outcome. Our method uses this model implicitly to compute causes and quantify the importance of different events with respect to the predicted outcome. We present a practical, model-agnostic algorithm that approximates event responsibility given the process structure reflected in the causal model. We evaluate our approach on a range of datasets derived from real-life event logs from a standard PPM benchmark. Each dataset contains up to 130,000 traces, with trace lengths of up to 1,800 events and up to 400 distinct event types. We compare our approach with state-of-the-art local explanation methods. The results demonstrate that our approach produces more stable and concise explanations while maintaining competitive efficiency.
CommentsAccepted to the 2026 IEEE International Conference on Data Mining (ICDM 2026)
