A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes
arXiv:2507.11178v3 Announce Type: replace-cross Abstract: With the advancement of deep learning technologies, various neural network-based Granger causality models have been proposed. Although these models have demonstrated notable improvements, several limitations remain. Most existing approaches adopt the component-wise architecture, necessitating the construction of a separate model for each time series, which results in substantial computational costs. In addition, imposing the sparsity-inducing penalty on the first-layer weights of the neural network to extract causal relationships weaken
The continuous advancements in deep learning necessitate more efficient and robust causal discovery methods to handle increasingly complex industrial and scientific datasets.
Improved causal discovery can lead to more effective decision-making, optimization, and fault identification in complex systems, directly impacting operational efficiency and predictive capabilities.
This framework offers a more computationally efficient and scalable approach to identifying causal relationships in time series data, potentially reducing the resource overhead for complex AI applications.
- · Industrial automation sector
- · Deep learning researchers
- · Complex systems operators
- · Inefficient causal discovery methods
- · Organizations reliant on manual causal analysis
More accurate and scalable causal insights become available for a broader range of applications.
Optimized industrial processes could lead to significant cost reductions and improved resource utilization across various sectors.
Enhanced understanding of system dynamics might accelerate the development of fully autonomous and adaptive industrial control systems.
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Read at arXiv cs.AI