CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models

TL;DR

CausalOps introduces a comprehensive lifecycle framework for developing and maintaining causal probabilistic graphical models, aiming to facilitate industrial adoption and standardize processes across various domains.

Contribution

This work presents the first integrated lifecycle framework for causal models, defining entities, workflows, and stakeholder roles to support practical implementation.

Findings

Defines a structured workflow for causal model development.

Establishes a common vocabulary for causal engineering.

Aims to promote industrial adoption of causal methods.

Abstract

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.