CGNet-PIE: A causal generative approach for urban planning intervention effect estimation

A critical challenge for urban planners lies in estimating the potential impacts of planning interventions before their implementation. Current approaches to urban planning intervention effect estimation encounter notable constraints. Association modelling approaches, while effective at capturing complex relationships between interventions and outcomes in observed cases, fail to estimate causal effects, making them unreliable for counterfactual predictions. Operational modelling approaches frequently set up structural causal models to transform interventions into outcomes based on subjective assumptions about simplified causal relationships. These approaches, while achieving a clear representation of causality, compromise the ability to accurately model the dynamic and intricate nature of cities and human behaviour. This paper introduces the Causal Generative Network for Planning Intervention Effect estimation (CGNet-PIE), a lightweight yet robust approach that directly estimates causal effects from observed data without the need for fully articulated causal interaction models. By integrating generative deep learning with the Rubin Causal Model, CGNet-PIE effectively captures non-linear relationships and enhances causal inference for reliable and generalizable counterfactual predictions. Experiments on synthetic, semi-synthetic, and real-world datasets demonstrate CGNet-PIE’s superior accuracy and generalization compared to baseline models. CGNet-PIE offers a powerful tool for planners to improve decision-making for informed policymaking and optimal resource allocation.