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在多个领域得到广泛应用，能够对不同领域的因果关系进行建模。随着其在汽车系统安全、机器学习等新兴领域中的逐步推广，业界对类似于DevOps和MLOps的集成生命周期框架的需求日益凸显。然而，目前尚缺乏适用于有意采用因果工程的组织的过程参考体系。为填补这一空白并促进工业界的广泛采用，我们提出了CausalOps——一种面向因果模型开发与应用的新型生命周期框架。通过定义因果工程中产生的关键实体、依赖关系及中间产物，我们建立了一套统一的术语和工作流模型。本研究将因果模型在多个阶段和利益相关方中的使用进行场景化，勾勒出模型创建与维护的整体视图。CausalOps旨在推动因果方法在相关组织及因果社区的实践应用中的落地。