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.

翻译：基于因果概率图的模型已获得广泛应用，能够对不同领域的因果关系进行建模。随着这些模型在汽车系统安全与机器学习等新领域的日益普及，对类似DevOps和MLOps的集成生命周期框架的需求也随之涌现。目前，对于有意采用因果工程的组织而言，尚缺乏一个流程参考。为填补这一空白并推动广泛的工业应用，我们提出了CausalOps——一个新颖的因果模型开发与应用生命周期框架。通过定义因果工程中生成的关键实体、依赖关系及中间产物，我们建立了一套一致的术语和工作流模型。本工作将因果模型的使用置于不同阶段和利益相关者的背景中，勾勒出创建和维护这些模型的整体视图。CausalOps的目标是推动因果方法在有意向的组织及因果社区中的实际应用。