Abstract
We refer to explainability as a system’s ability to provide sound and human-understandable insights concerning its outcomes. Explanations should accurately reflect causal relations in process executions. This abstract suggests augmenting process discovery (PD) with causal process discovery (CD) to generate causal-process-execution narratives. These narratives serve as input for large language models (LLMs) to derive sound and human-interpretable explanations. A multi-layered knowledge-graph is employed to facilitate diverse process views.