  The transformation of urban environments to accommodate growing populations
has profoundly impacted public health and well-being. This paper addresses the
critical challenge of estimating the impact of urban design interventions on
diverse populations. Traditional approaches, reliant on questionnaires and
stated preference techniques, are limited by recall bias and capturing the
complex dynamics between environmental attributes and individual
characteristics. To address these challenges, we integrate Virtual Reality (VR)
with observational causal inference methods to estimate heterogeneous treatment
effects, specifically employing Targeted Maximum Likelihood Estimation (TMLE)
for its robustness against model misspecification. Our innovative approach
leverages VR-based experiment to collect data that reflects perceptual and
experiential factors. The result shows the heterogeneous impacts of urban
design elements on public health and underscore the necessity for personalized
urban design interventions. This study not only extends the application of TMLE
to built environment research but also informs public health policy by
illuminating the nuanced effects of urban design on mental well-being and
advocating for tailored strategies that foster equitable, health-promoting
urban spaces.
