Data-driven methods for landslide susceptibility assessment (LSA) often suffer from spurious correlations and “black-box” opacity, failing to capture the spatial dependency processes underlying landslide development. To address these limitations, we propose a directed acyclic graph (DAG)-informed interpretable framework by integrating structure-learning algorithms and graph attention models. This approach enables the identification of spatial dependency pathways and quantifies the propagation magnitudes (weights of connected links) of landslide conditioning factors. We applied this framework to the Ili River Basin, Xinjiang, China. A total of 14 robust spatial dependency chains were identified, and the dominant susceptibility-related chains were categorized into four types: (1) Elevation–climate-driven pathways (Elevation → Precipitation → NDWI → Landslide; Elevation → Precipitation → Temperature → Snow Depth → NDWI → Landslide); (2) Tectonic-controlled pathways (Distance to faults → PGA → Landslide); (3) Topographic dominated pathways (Slope → Curvature → Landslide); and (4) Hydrological driven pathways (Distance to rivers → NDWI → Landslide). Using a novel importance-weighted decoupling method, we generated pathway-specific susceptibility maps. These four chains account for 18.32%, 15.74%, 17.67%, and 16.76% of the high-susceptibility areas, respectively. These areas are predominantly clustered in mid–high mountainous, high-intensity seismic, and weakened lithological belt regions. Our proposed framework advances LSA from statistical prediction to dependency-informed explanation, providing decision-makers with a scientific basis for interpreting susceptibility variations across different spatial and environmental settings.