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We refer to explainability as a system’s ability to provide sound and humanunderstandable insights concerning its outcomes. Explanations should accurately reflect causalrelations in process executions [1]. 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-interpretableexplanations. A multi-layered knowledge-graph is employed to facilitate diverse process views.