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

arXiv: https://arxiv.org/abs/2308.01375 · HTML: https://mlsanity.com/paper/2308.01375/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 93 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms](https://mlsanity.com/paper/1901.10912/) (2019)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [The Causal-Neural Connection: Expressiveness, Learnability, and Inference](https://mlsanity.com/paper/2107.00793/) (2021)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [On the Logic of Causal Models](https://mlsanity.com/paper/1304.2355/) (2013)
- [A Probabilistic Calculus of Actions](https://mlsanity.com/paper/1302.6835/) (2013)
- [BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning](https://mlsanity.com/paper/2003.01207/) (2020)
- [A Survey of DevOps Concepts and Challenges](https://mlsanity.com/paper/1909.05409/) (2019)
- [Applications of statistical causal inference in software engineering](https://mlsanity.com/paper/2211.11482/) (2022)
- [Quantitative probing: Validating causal models using quantitative domain knowledge](https://mlsanity.com/paper/2209.03013/) (2022)
- [Testing Causality in Scientific Modelling Software](https://mlsanity.com/paper/2209.00357/) (2022)
- [Boundary Objects and their Use in Agile Systems Engineering](https://mlsanity.com/paper/1904.12131/) (2019)
- [Grasping Causality for the Explanation of Criticality for Automated Driving](https://mlsanity.com/paper/2210.15375/) (2022)
- [Leveraging Data and Analytics for Digital Business Transformation through DataOps: An Information Processing Perspective](https://mlsanity.com/paper/2201.09617/) (2022)

## Abstract (arXiv)

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.