用Transformer估计时空反事实结果，提升因果推断精度
Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
- 基于Transformer建模时空依赖关系，替代传统统计方法
- 模拟与真实数据实验均显示优于基线方法，尤其在复杂时空模式下
- 适合研究时空因果效应的学者，如环境、公共政策领域
现实世界天然具有时空维度，因此估计具有时空属性的反事实结果是一项关键问题。然而，以往方法依赖经典统计模型，在性能和泛化能力上仍有局限。本文提出一种基于Transformer的新框架，用于估计具有时空属性的反事实结果，展现出更强的估计能力。在弱假设下，所提出的估计器具有一致性和渐近正态性。通过模拟实验和真实数据实验验证方法有效性：模拟实验表明该估计器比基线方法更具估计优势；真实数据实验揭示了冲突对哥伦比亚森林损失的因果影响，提供了有价值的结论。源代码已公开于 https://github.com/lihe-maxsize/DeppSTCI_Release_Version-master。
原文摘要 · Abstract (English)
The real world naturally has dimensions of time and space. Therefore, estimating the counterfactual outcomes with spatial-temporal attributes is a crucial problem. However, previous methods are based on classical statistical models, which still have limitations in performance and generalization. This paper proposes a novel framework for estimating counterfactual outcomes with spatial-temporal attributes using the Transformer, exhibiting stronger estimation ability. Under mild assumptions, the proposed estimator within this framework is consistent and asymptotically normal. To validate the effectiveness of our approach, we conduct simulation experiments and real data experiments. Simulation experiments show that our estimator has a stronger estimation capability than baseline methods. Real data experiments provide a valuable conclusion to the causal effect of conflicts on forest loss in Colombia. The source code is available at https://github.com/lihe-maxsize/DeppSTCI_Release_Version-master.
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