提出ProCause生成反事实结果，提升实时流程干预评估的准确性。
ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
- 融合多种因果推断模型与时序结构，支持序列与非序列数据
- 实验证明集成模型比单一TARNet更稳定，时序模型在有依赖时更优
- 适用于流程监控中需评估干预效果的研究者与工业应用
Prescriptive Process Monitoring（PresPM）关注基于事件日志数据进行实时干预以优化流程。由于数据集中缺乏所有干预动作的真实结果，评估方法面临挑战。现有基于因果推断的生成方法RealCause忽视过程数据中的时序依赖，且仅依赖单一模型TARNet，影响效果。为此，我们提出ProCause，支持序列模型（如LSTM）与非序列模型，并整合S-Learner、T-Learner、TARNet及集成模型。通过具有已知真实结果的模拟器研究发现，TARNet并非始终最优，集成模型更具一致性；引入LSTM在存在时序依赖时显著提升评估性能。进一步在真实数据上验证了ProCause的有效性，实现对PresPM方法更可靠的评估。
原文摘要 · Abstract (English)
Prescriptive Process Monitoring (PresPM) is the subfield of Process Mining that focuses on optimizing processes through real-time interventions based on event log data. Evaluating PresPM methods is challenging due to the lack of ground-truth outcomes for all intervention actions in datasets. A generative deep learning approach from the field of Causal Inference (CI), RealCause, has been commonly used to estimate the outcomes for proposed intervention actions to evaluate a new policy. However, RealCause overlooks the temporal dependencies in process data, and relies on a single CI model architecture, TARNet, limiting its effectiveness. To address both shortcomings, we introduce ProCause, a generative approach that supports both sequential (e.g., LSTMs) and non-sequential models while integrating multiple CI architectures (S-Learner, T-Learner, TARNet, and an ensemble). Our research using a simulator with known ground truths reveals that TARNet is not always the best choice; instead, an ensemble of models offers more consistent reliability, and leveraging LSTMs shows potential for improved evaluations when temporal dependencies are present. We further validate ProCause's practical effectiveness through a real-world data analysis, ensuring a more reliable evaluation of PresPM methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。