Shifting the paradigm: Estimating heterogeneous treatment effects in the development of walkable cities design

Abstract

Urban transformations driven by population growth and aging have significantly impacted public health and well-being. Estimating the heterogeneous effects of urban design interventions remains challenging, as traditional methods—such as questionnaires and stated preferences—suffer from recall bias and oversimplify interactions between environmental attributes and individual characteristics. This study addresses these limitations by integrating Virtual Reality (VR) with Targeted Maximum Likelihood Estimation (TMLE) to generate robust causal estimates. VR provides an immersive, controlled platform for capturing perceptual and experiential responses, while TMLE mitigates selection bias and estimates Conditional Average Treatment Effects (CATE) across demographic subgroups. Our findings reveal heterogeneous impacts of key urban design attributes—land use mix, block connectivity, road size, open space, and greenery—on perceived walkability and emotional responses. Open space and greenery consistently produced positive effects, while interaction effects between attributes highlighted the need for context-sensitive planning. By applying TMLE to VR-based conjoint experiments, this study advances-built environment research and provides actionable insights for public health policy, emphasizing the importance of personalized urban design strategies that foster equitable, health-supportive environments.