Causally-Guided Pairwise Transformer -- Towards Foundational Digital Twins in Process Industry

• URL: http://arxiv.org/abs/2508.13111v2
• Date: Mon, 22 Sep 2025 07:58:26 GMT
• Title: Causally-Guided Pairwise Transformer -- Towards Foundational Digital Twins in Process Industry
• Authors: Michael Mayr, Georgios C. Chasparis,
• Abstract summary: Causally-Guided Pairwise Transformer (CGPT) is a novel architecture that integrates a known causal graph as an inductive bias.<n>We show that CGPT significantly outperforms both channel-dependent (CD) and channel-independent (CI) models in predictive accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Foundational modelling of multi-dimensional time-series data in industrial systems presents a central trade-off: channel-dependent (CD) models capture specific cross-variable dynamics but lack robustness and adaptability as model layers are commonly bound to the data dimensionality of the tackled use-case, while channel-independent (CI) models offer generality at the cost of modelling the explicit interactions crucial for system-level predictive regression tasks. To resolve this, we propose the Causally-Guided Pairwise Transformer (CGPT), a novel architecture that integrates a known causal graph as an inductive bias. The core of CGPT is built around a pairwise modeling paradigm, tackling the CD/CI conflict by decomposing the multidimensional data into pairs. The model uses channel-agnostic learnable layers where all parameter dimensions are independent of the number of variables. CGPT enforces a CD information flow at the pair-level and CI-like generalization across pairs. This approach disentangles complex system dynamics and results in a highly flexible architecture that ensures scalability and any-variate adaptability. We validate CGPT on a suite of synthetic and real-world industrial datasets on long-term and one-step forecasting tasks designed to simulate common industrial complexities. Results demonstrate that CGPT significantly outperforms both CI and CD baselines in predictive accuracy and shows competitive performance with end-to-end trained CD models while remaining agnostic to the problem dimensionality.

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