Root cause analysis is critical in manufacturing for identifying and addressing the underlying factors that lead to defects, inefficiencies, and failures. By pinpointing the true sources of problems, manufacturers can implement targeted solutions to prevent recurrence, enhance product quality, reduce waste, and improve operational efficiency.
Traditional correlation-based machine learning techniques, which many companies rely on, face significant limitations in root cause analysis due to their inability to capture causality. These methods often fail to differentiate true root causes from symptoms, oversimplify complex manufacturing processes into tabular data, and neglect the manufacturing process flow. As a result, they risk producing misleading conclusions.
Causal machine learning addresses these challenges by modeling cause-and-effect relationships within complex production processes, moving beyond simple correlations. Leveraging domain knowledge, often represented as knowledge graphs, it captures causal relationships among key variables in manufacturing. This approach enables a clearer distinction between root causes and symptoms, allowing for more accurate identification of issues and their origins.
In this series of notebooks, we demonstrate how causal machine learning techniques can be applied to perform root cause analysis in manufacturing. Using a fictitious scenario where we manage a production line, we explore how various factors affect the quality of processed products, providing a practical introduction to this powerful technique.
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© 2025 Databricks, Inc. All rights reserved. The source in this notebook is provided subject to the Databricks License [https://databricks.com/db-license-source]. All included or referenced third party libraries are subject to the licenses set forth below.
| library | description | license | source |
|---|---|---|---|
| Graphviz | An open source graph visualization software | Common Public License Version 1.0 | https://graphviz.org/download/ |
| pygraphviz | A Python interface to the Graphviz graph layout and visualization package | BSD | https://pypi.org/project/pygraphviz/ |
| networkx | A Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. | BSD | https://pypi.org/project/networkx/ |
| dowhy | A Python library for causal inference that supports explicit modeling and testing of causal assumptions | MIT | https://pypi.org/project/dowhy/ |
| causal-learn | A python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and extension of Tetrad. | MIT | https://pypi.org/project/causal-learn/ |
| lime | Local Interpretable Model-Agnostic Explanations for machine learning classifiers | BSD | https://pypi.org/project/lime/ |
| shap | A unified approach to explain the output of any machine learning model | MIT | https://pypi.org/project/shap/ |
