Urban Climate Counterfactuals: A Causal Dataset for Street-Level Heat Mitigation Interventions

UrbanCLIM-Causal enables AI to answer: Which $20k cooling intervention reduces heat deaths most in this district? - by providing the first openly shareable, causally rich dataset linking urban interventions to street level microclimate impacts. By combining satellite imagery, ground sensors and physics-based counterfactual simulations, it quantifies cooling effects of interventions (e.g., tree planting, cool roofs). The dataset aims to accelerate robust, policy-guiding AI tools for climate science, urban planning and public health, with equitable coverage and global applicability.