  In the field of intracity freight transportation, changes in order volume are
significantly influenced by temporal and spatial factors. When building subsidy
and pricing strategies, predicting the causal effects of these strategies on
order volume is crucial. In the process of calculating causal effects,
confounding variables can have an impact. Traditional methods to control
confounding variables handle data from a holistic perspective, which cannot
ensure the precision of causal effects in specific temporal and spatial
dimensions. However, temporal and spatial dimensions are extremely critical in
the logistics field, and this limitation may directly affect the precision of
subsidy and pricing strategies. To address these issues, this study proposes a
technique based on flexible temporal-spatial grid partitioning. Furthermore,
based on the flexible grid partitioning technique, we further propose a
continuous entropy balancing method in the temporal-spatial domain, which named
TS-EBCT (Temporal-Spatial Entropy Balancing for Causal Continue Treatments).
The method proposed in this paper has been tested on two simulation datasets
and two real datasets, all of which have achieved excellent performance. In
fact, after applying the TS-EBCT method to the intracity freight transportation
field, the prediction accuracy of the causal effect has been significantly
improved. It brings good business benefits to the company's subsidy and pricing
strategies.

Methodology;Artificial Intelligence;Machine Learning