Understanding Deterioration Random Effects for Causal Discovery in Infrastructure Management
Infrastructure deterioration poses significant challenges for asset management, yet existing approaches rely on population-averaged models that overlook equipment-specific heterogeneity. We present a novel framework that combines Bayesian hierarchical hazard modeling with causal discovery to identify operational patterns that drive heterogeneous deterioration rates in pump equipment. Our approach first estimates pump-specific random effects $u_i$ using GPU-accelerated No-U-Turn Sampling (NUTS), achieving 3--5$\times$ speedup over CPU implementations. We then employ DirectLiNGAM to discover causal relationships between 22 engineered time-series features and deterioration rates, stratified ...
| Authors | Takato Yasuno |
|---|---|
| Primary category | stat.AP |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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