统一多版本流程的因果模型，显式表达交替执行路径。
The WHY in Business Processes: Unification of Causal Process Models
- 提出新方法整合多个因果流程变体为一致模型
- 在3个公开+2个私有数据集上验证有效性
- 适合需要理解复杂流程交替逻辑的业务分析者
因果推理对业务流程干预与优化至关重要，需明确事件日志中活动执行时间间的因果关系。现有方法虽能发现因果流程模型，但无法捕捉多变体间的交替因果条件，导致处理缺失值及日志拆分时的交替关系表达困难。本文提出一种新方法，将多个因果流程变体统一为一致性模型，既保持原模型正确性，又显式表示其因果流交替。方法经形式化定义并证明，已在三个公开和两个私有数据集上评估，并开源实现。
原文摘要 · Abstract (English)
Causal reasoning is essential for business process interventions and improvement, requiring a clear understanding of causal relationships among activity execution times in an event log. Recent work introduced a method for discovering causal process models but lacked the ability to capture alternating causal conditions across multiple variants. This raises the challenges of handling missing values and expressing the alternating conditions among log splits when blending traces with varying activities. We propose a novel method to unify multiple causal process variants into a consistent model that preserves the correctness of the original causal models, while explicitly representing their causal-flow alternations. The method is formally defined, proved, evaluated on three open and two proprietary datasets, and released as an open-source implementation.
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