提出融合反事实与事实推理的解释方法，提升时序事件模型的可解释性。
Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning
- 结合反事实与事实推理，定义更合理的时序事件解释
- 在真实数据集上验证，解释质量优于基线方法
- 适用于医疗、金融等高风险场景的模型可信度分析
基于神经网络的带标记时序点过程（MTPP）模型广泛应用于高风险场景，其输出可信度引发关注。本文研究MTPP的解释问题，目标是找到最小且合理的解释——即历史事件中一个最小子集，使得仅基于该子集的预测精度接近甚至优于使用全历史数据的精度，同时优于使用该子集补集的精度。研究发现，单纯采用反事实或事实解释会导致不合理结果。为此，本文将解释定义为反事实与事实解释的结合，并提出计数反事实与事实解释器（CFF），通过一系列精心设计的技术实现高效解释。实验表明，CFF在解释质量与计算效率方面均显著优于基线方法。
原文摘要 · Abstract (English)
Neural network-based Marked Temporal Point Process (MTPP) models have been widely adopted to model event sequences in high-stakes applications, raising concerns about the trustworthiness of outputs from these models. This study focuses on Explanation for MTPP, aiming to identify the minimal and rational explanation, that is, the minimum subset of events in history, based on which the prediction accuracy of MTPP matches that based on full history to a great extent and better than that based on the complement of the subset. This study finds that directly defining Explanation for MTPP as counterfactual explanation or factual explanation can result in irrational explanations. To address this issue, we define Explanation for MTPP as a combination of counterfactual explanation and factual explanation. This study proposes Counterfactual and Factual Explainer for MTPP (CFF) to solve Explanation for MTPP with a series of deliberately designed techniques. Experiments demonstrate the correctness and superiority of CFF over baselines regarding explanation quality and processing efficiency.
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