Process mining is widely used to diagnose processes and uncover performance and compliance problems. It is also possible to see relations between different behavioral aspects, e.g., cases that deviate more at the beginning of the process tend to get delayed in the last part of the process. However, correlations do not necessarily reveal causalities. Moreover, standard process mining diagnostics do not indicate how to improve the process. This is the reason we advocate the use of \emph{structural equation models} and \emph{counterfactual reasoning}. We use results from causal inference and adapt these to be able to reason over event logs and process interventions. We have implemented the approach as a ProM plug-in and have evaluated it on several data sets. Our ProM plug-in produces recommendations that indicate how specific cases could have been handled differently to avoid a performance or compliance problem.

中文翻译：

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过程挖掘中的案例级反事实推理

流程挖掘被广泛用于诊断流程并发现性能和合规性问题。也有可能看到不同行为方面之间的关系，例如，在流程开始时偏离更大的案例在流程的最后部分往往会延迟。但是，相关性不一定揭示因果关系。而且，标准的过程挖掘诊断程序并未指示如何改进过程。这就是我们主张使用\ emph {结构方程模型}和\ emph {反事实推理}的原因。我们使用因果推断的结果，并对结果进行调整，以便能够对事件日志和过程干预进行推理。我们已经将该方法实现为ProM插件，并已在多个数据集上对其进行了评估。