  Causal probabilistic graph-based models have gained widespread utility,
enabling the modeling of cause-and-effect relationships across diverse domains.
With their rising adoption in new areas, such as automotive system safety and
machine learning, the need for an integrated lifecycle framework akin to DevOps
and MLOps has emerged. Currently, a process reference for organizations
interested in employing causal engineering is missing. To address this gap and
foster widespread industrial adoption, we propose CausalOps, a novel lifecycle
framework for causal model development and application. By defining key
entities, dependencies, and intermediate artifacts generated during causal
engineering, we establish a consistent vocabulary and workflow model. This work
contextualizes causal model usage across different stages and stakeholders,
outlining a holistic view of creating and maintaining them. CausalOps' aim is
to drive the adoption of causal methods in practical applications within
interested organizations and the causality community.
