Identifying Wider Barrier Effects of Ground-Level Transport Infrastructure through Spatial Causal Inference

Abstract

This study examined the wider barrier effects generated by ground-level urban expressways and railways. To address spatial confounding, spatial dependence, and spatial spillover in a cross-sectional causal-inference design, this study developed a spatial causalinference framework that combines a multi-treatment design with Double Machine Learning and Gaussian-process-based nuisance learning. Using Seoul’s ground-level transport networks and officially assessed land prices, the analysis found that the direct effects of transport infrastructure were mostly insignificant, whereas spillover effects were significant in most areas and reduced officially assessed land prices by an average of 11.12%. The results also showed that land-value declines were particularly pronounced in peripheral and relatively marginalized areas of Seoul, suggesting that transport infrastructure accelerates decline in already disadvantaged neighborhoods.