AI 中文总结
针对时空数据中隐藏混淆与干扰导致的因果效应估计难题，本文提出时空近端因果推断框架，结合Transformer时空编码器等技术，在合成数据集上取得与基线方法相当的性能。
AI 中文摘要
从现实世界的时空数据中估计因果效应颇具挑战性，原因在于存在隐藏混淆变量与干扰。标准因果识别方法假设在给定观测协变量的条件下存在条件可交换性，当隐藏混淆变量同时影响处理变量和结果变量时，该假设不再成立——这一情况常见于气候、环境政策、流行病学和区域经济学等领域。本文中，我们提出一种新颖的时空近端因果推断框架，将近端识别理论扩展至时空场景。该方法通过引入处理诱导代理变量和结果诱导代理变量，联合捕捉局部及邻域层面的混淆信息，我们推导得到一种时空结果混淆桥函数，该函数无需直接恢复隐藏混淆变量即可识别潜在结果。我们在代理排除约束和时空完备性条件下证明了该桥函数的可识别性，并表明所得估计量通过g计算公式的近端泛化形式恢复结果。为将该识别结果付诸实践，我们提出一种神经架构，该架构通过基于Transformer的时空编码器学习代理变量，同时耦合一个条件互信息评判器以强制执行排除约束，以及一个矩匹配网络以确保学习到的桥函数满足基础识别方程。我们进一步引入一种稳定化加权方案以解决处理支持不平衡问题。在合成数据集上的实验表明，我们的方法取得了与基线因果推断方法相当的性能，据我们所知，这是首个通过近端因果推断框架为存在时空干扰时的隐藏混淆提供理论依据的结果。
英文摘要
Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference. Standard causal identification methods assume conditional exchangeability given observed covariates, which fails whenever hidden confounders affect both treatment and outcomes - a common setting in domains such as climate, environmental policy, epidemiology, and regional economics. In this paper, we propose a novel spatiotemporal proximal causal inference framework that extends proximal identification theory to spatiotemporal settings. The proposed method jointly captures local and neighborhood-level confounding information by introducing treatment- and outcome-inducing proxies, and we derive a spatiotemporal outcome confounding bridge function that identifies the potential outcome without requiring direct recovery of the hidden confounder. We establish the identifiability of this bridge function under proxy exclusion restrictions and a spatiotemporal completeness condition, and show that the resulting estimator recovers the outcome through a proximal generalization of the g-computation formula. To operationalize this identification result, we propose a neural architecture that learns proxies via transformer-based spatiotemporal encoders - coupled with a conditional mutual information critic to enforce exclusion restrictions and a moment-matching network to guarantee that the learned bridge function satisfies the underlying identifying equation. We further introduce a stabilized weighting scheme to address treatment support imbalance. Experiments on synthetic datasets demonstrate that our approach achieves comparable performance to baseline causal inference methods, while providing, to our knowledge, the first theoretically grounded outcomes for the hidden confounding in the presence of spatiotemporal interference through a proximal causal inference framework.
