The automated production of individualized products in a cyber-physical production system (CPPS) requires the combined automation of software and machine components. While this leads to increased productivity, the complexity of the CPPS may result in long unplanned downtimes when faults occur, and no system model is available to guide the maintenance team. Knowledge-driven, data-driven or hybrid modeling are available approaches in the literature to obtaining a system model. While expert-driven and data-driven modeling face limited applicability to CPPS, hybrid models, combining both approaches can offer a solution. This paper proposes a causality-driven hybrid model for fault diagnosis in complex CPPS, represented in a causal knowledge graph (CKG). The CKG serves as a transparent system model for collaborative human-machine fault diagnosis. We provide a concept for the continuous hybrid learning of the CKG, a maturity model to classify the resulting CKG's fault diagnosis capabilities, and the industrial setting inspiring the approach.