As commuting rail networks expand and passenger demand grows, service delays have become a growing challenge, propagating through the network and undermining system reliability. Prevailing research, often reliant on statistical correlations or 'black-box' predictive models, fails to reveal the causal mechanisms of delay propagation. To address this gap, this study proposes a network-centric approach grounded in causal inference to explicitly map the directional pathways of delay.Focusing on the Stockholm commuter rail system, we employ the Peter and Clark Momentary Conditional Independence (PCMCI) algorithm on real-world time-series data to construct a Delay Propagation Causal Network (DPCN). Our multi-stage analysis of the DPCN reveals a highly structured network where delays propagate along stable, predictable pathways. A novel classification identifies four distinct station roles with a clear core-periphery spatial logic. To identify the most critical nodes, we introduce a composite causal delay impact index, which integrates causal strength with real-world delay probabilities and successfully identifies high-impact station clusters that align with peak-hour commuter traffic. A final comparison illustrates the advantages of a causality-based approach over correlation-based methods in distinguishing causal propagation links from spurious associations. This study presents a generalizable, causality-based framework and practical tools for transit authorities, offering a data-driven foundation for proactive network management. It enables operators to identify and mitigate systemic vulnerabilities, thereby enhancing the efficiency, reliability, and resilience of commuter rail systems.