Causal Digital Twins: Real-Time Counterfactuals for Industrial Process Optimization

Abstract

Digital twin platforms have become commonplace in process industries, yet most rely on correlation-based simulators that provide limited insight into causal mechanisms. This article introduces the Causal Digital Twin (CDT), an architecture that combines structural-causal models with high-frequency sensor streams to generate counterfactual answers in near real-time. A cluster of graphics-processing units executes constraint and score-based discovery across billion-scale graphs, while a sliding-window engine keeps parameters fresh as conditions evolve. Field evaluations demonstrate substantial reductions in energy consumption and unplanned downtime, accompanied by alert latencies that approach the cadence of plant-control loops. The discussion outlines system design, governance safeguards, and empirical evidence that causal reasoning shortens operator troubleshooting cycles and strengthens trust in automated recommendations.