解决时空数据中隐藏混杂因素的因果推断难题
Spatio-Temporal Hierarchical Causal Models
- 构建时空层次因果模型，显式建模单位级隐藏混杂因子
- 理论证明随子单元数据增加，复杂模型可简化为可识别的平滑模型
- 适用于交通网络等动态系统，对传统方法失效场景仍有效
海量细粒度时空数据（如交通传感器网络）为科学发现提供了巨大机遇。然而，从观测数据中推断因果关系仍具挑战性，尤其因特定于单元（如地理区域）且随时间影响结果的未观测混杂因子存在。现有大多数时空因果推断方法假设所有混杂因子均被观测到，这一假设在实践中常被违背。本文提出时空层次因果模型（ST-HCM），将层次因果建模扩展至时空领域。核心是时空坍缩定理，表明当子单元数据量增加时，复杂的ST-HCM会收敛至更简单的扁平因果模型。该理论结果支持通用因果识别程序，使ST-HCM能在存在未观测、时不变单元级混杂因子的情况下恢复因果效应，而标准非层次模型在此情形下失效。我们在合成与真实世界数据集上验证了框架的有效性，展示了其在复杂动态系统中稳健因果推断的潜力。
原文摘要 · Abstract (English)
The abundance of fine-grained spatio-temporal data, such as traffic sensor networks, offers vast opportunities for scientific discovery. However, inferring causal relationships from such observational data remains challenging, particularly due to unobserved confounders that are specific to units (e.g., geographical locations) yet influence outcomes over time. Most existing methods for spatio-temporal causal inference assume that all confounders are observed, an assumption that is often violated in practice. In this paper, we introduce Spatio-Temporal Hierarchical Causal Models (ST-HCMs), a novel graphical framework that extends hierarchical causal modeling to the spatio-temporal domain. At the core of our approach is the Spatio-Temporal Collapse Theorem, which shows that a complex ST-HCM converges to a simpler flat causal model as the amount of subunit data increases. This theoretical result enables a general procedure for causal identification, allowing ST-HCMs to recover causal effects even in the presence of unobserved, time-invariant unit-level confounders, a scenario where standard non-hierarchical models fail. We validate the effectiveness of our framework on both synthetic and real-world datasets, demonstrating its potential for robust causal inference in complex dynamic systems.
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