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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.01375 |
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| _version_ | 1866916312572231680 |
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| author | Maier, Robert Schlattl, Andreas Guess, Thomas Mottok, Jürgen |
| author_facet | Maier, Robert Schlattl, Andreas Guess, Thomas Mottok, Jürgen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_01375 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models Maier, Robert Schlattl, Andreas Guess, Thomas Mottok, Jürgen Artificial Intelligence H.1 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. |
| title | CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models |
| topic | Artificial Intelligence H.1 |
| url | https://arxiv.org/abs/2308.01375 |