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.

翻译：在城市内货运领域，订单量的变化受时空因素显著影响。在制定补贴与定价策略时，预测这些策略对订单量的因果效应至关重要。计算因果效应的过程中，混杂变量会产生干扰。传统控制混杂变量的方法从整体视角处理数据，无法确保特定时空维度下因果效应的精度。然而，时空维度在物流领域极为关键，这一局限可能直接影响补贴与定价策略的精准度。针对这些问题，本研究提出一种基于灵活时空网格划分的技术。进一步，基于灵活网格划分技术，我们提出了一种时空域连续熵平衡方法，命名为TS-EBCT（时空熵平衡因果连续处理效应估计）。本文提出的方法在两个模拟数据集和两个真实数据集上进行了测试，均取得了优异性能。实际应用于城市内货运领域后，因果效应的预测精度显著提升，为企业的补贴与定价策略带来了良好的商业效益。