Public bus transit operates within an intricate network of routes and stops, where delays are common and can propagate throughout the transit system, affecting systemreliability, passenger satisfaction, and operational efficiency. Existing research on bus delay propagation has primarily focused on route-level delays correlation-basedanalysis, lacking a comprehensive understanding of underlying causal mechanisms of bus delay propagation from a network-level perspective. To enhance our understanding of bus delay propagation within urban transit systems, this study aims to develop a new approach that captures the causal relationshipsbetween stop delays, integrating their temporal and spatial characteristics. Utilizing a causal discovery algorithm for time series data, the thesis infers causal relationshipsfrom bus operation time series data. It then analyze the resulting causal graphs based on complex network measurement indicators. A case study using GTFS data of Stockholm, Sweden, was conducted. The results reveal that stops with a high degree of connections significantly influence delay propagation, with the network exhibiting a community structure that includes both high-degree and low-degree stops. Stops are classified based on their levels into four distinct delay propagation patterns. Critical stops are identified as either delay aggravation or absorption stops, based on their Momentary Conditional Independence (MCI) values. A new metric was constructed, underscoring the importance of considering delays across the entire network rather than isolating analysis to individual routes. The comparison with traditional correlation-based analysis highlights instances of low correlation among stops with high causality and high correlations without underlying causality, emphasizing the deeper insight from the causal approach