Causal Inference Model for Interpretable Analysis of Travel Behavior

This study proposes a deep causal inference framework, CAROLINA, that integrates causal discovery, structural causal modeling, and deep learning to enable interpretable analysis and counterfactual forecasting of travel behavior.