In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes. Furthermore, the relational time-series causal inference models are able to estimate local effects for individual nodes by exploiting local node-centric temporal dependencies and topological/structural dependencies. We show that simpler causal models that do not consider the graph topology are recovered as special cases of the proposed relational time-series causal inference model. We describe the conditions under which the resulting estimate can be used to estimate a causal effect, and describe how the Durbin-Wu-Hausman test of specification can be used to test for the consistency of the proposed estimator from data. Empirically, we demonstrate the effectiveness of the causal inference models on both synthetic data with known ground-truth and a large-scale observational relational time-series data set collected from Wikipedia.

中文翻译：

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从基于图的关系时间序列推断个体层次因果模型

在这项工作中，我们将基于图的关系时间序列数据的因果推理问题形式化，其中图中的每个节点都有一个或多个与之相关的时间序列。我们为这个问题提出了因果推理模型，利用图拓扑和时间序列来准确估计节点的局部因果效应。此外，关系时间序列因果推理模型能够通过利用以本地节点为中心的时间依赖性和拓扑/结构依赖性来估计单个节点的局部影响。我们表明，不考虑图拓扑的更简单的因果模型被恢复为所提出的关系时间序列因果推理模型的特例。我们描述了可以使用所得估计值来估计因果效应的条件，并描述如何使用 Durbin-Wu-Hausman 规范检验来测试建议的数据估计量的一致性。从经验上讲，我们证明了因果推理模型对具有已知地面实况的合成数据和从维基百科收集的大规模观察关系时间序列数据集的有效性。