Unifying graph neural networks causal machine learning and conformal prediction for robust causal inference in rail transport systems

Modeling the operational dynamics of intricate rail transit systems faces three significant challenges: addressing network-wide dependencies, differentiating correlation from causation, and accurately quantifying prediction uncertainty. Current methodologies generally tackle these issues separately, resulting in models that are either structurally simplistic, causally unclear, or overly confident in their forecasts. This paper presents a comprehensive framework that, for the first time, effectively combines Graph Neural Networks (GNNs), Causal Machine Learning (CML), and Conformal Prediction (CP) to resolve this dilemma. GNNs are utilized to capture the topological dependencies within the rail network, CML is employed to discern the unbiased causal impacts of operational interventions, and CP offers mathematically assured, distribution-free uncertainty intervals. Our empirical assessment using real-world operational data reveals a distinct differentiation in model performance: while GNN-enhanced hybrids excel in aggregate prediction tasks (CV R² ≈ 0.87), the proposed CML-CP framework realizes a transformative, order-of-magnitude decrease in causal effect estimation error (CV MAE: 124,758.04). Thus, the primary contribution of this research is not merely a singular "best" model, but rather a methodological roadmap that facilitates a paradigm shift from reactive data modeling to a proactive, strategic decision-support tool. This framework equips decision-makers to perform reliable what-if scenario analyses, supported by robust causal insights and valid uncertainty guarantees, leading to more resilient and efficient railway operations.