A Multi-level Causal Inference Method for Population Downscaling

This study develops a new approach, multi-level causal Bayesian network spatialization (MCBNS), which integrates causal inference with multi-level modeling. This method employs structural causal models to analyze direct and indirect relationships between population distribution and influencing factors at the county level, establishing stable causal relationships. It then refines these relationships using data from selected townships to build multi-level models for more accurate prediction.