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[2512.01149] A Benchmark of Causal vs. Correlation AI for Predictive Maintenance
Summary
This paper benchmarks causal versus correlation-based AI methods for predictive maintenance, revealing that while correlation models excel in cost savings, causal models provide better interpretability and failure attribution.
Why It Matters
Understanding the differences between causal and correlation-based AI is crucial for industries relying on predictive maintenance. The findings highlight how causal methods can enhance operational decision-making by providing insights into failure causes, potentially leading to more effective maintenance strategies and reduced costs.
Key Takeaways
- Causal models can outperform correlation models in interpretability and failure attribution.
- Correlation-based models achieved a 70.8% cost reduction, while causal models offered 66.4%.
- The study utilized a dataset of 10,000 CNC machines to benchmark various predictive models.
- Causal methods combined with domain knowledge can improve predictive maintenance outcomes.
- Operational realities necessitate a shift from purely statistical accuracy to causal understanding.
Computer Science > Artificial Intelligence arXiv:2512.01149 (cs) [Submitted on 30 Nov 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:A Benchmark of Causal vs. Correlation AI for Predictive Maintenance Authors:Shaunak Dhande, Chutian Ma, Giacinto Paolo Saggese, Paul Smith, Krishna Taduri View a PDF of the paper titled A Benchmark of Causal vs. Correlation AI for Predictive Maintenance, by Shaunak Dhande and 4 other authors View PDF HTML (experimental) Abstract:Predictive maintenance in manufacturing environments presents a challenging optimization problem characterized by extreme cost asymmetry, where missed failures incur costs roughly fifty times higher than false alarms. Predictive maintenance in manufacturing environments presents a challenging optimization problem characterized by extreme cost asymmetry, where missed failures incur costs roughly fifty times higher than false alarms. Conventional machine learning approaches typically optimize statistical accuracy metrics that do not reflect this operational reality and cannot reliably distinguish causal relationships from spurious correlations. This study benchmarks eight predictive models, ranging from baseline statistical approaches to Bayesian structural causal methods, on a dataset of 10,000 CNC machines with a 3.3 percent failure prevalence. While ensemble correlation-based models such as Random Forest (L4) achieve the highest raw cost savings (70.8 percent reduction), the Bayesian Structural Causal ...
