论文标题
从基于图的关系时间序列中推断个人级别的因果模型
Inferring Individual Level Causal Models from Graph-based Relational Time Series
论文作者
论文摘要
在这项工作中，我们对基于图的关系时间序列数据进行了正式的因果推断问题，其中图中的每个节点都具有与之相关的一个或多个时间序列。我们提出了这个问题的因果推断模型，该模型利用图形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局部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，不考虑图形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殊情况。我们描述了所得估计值可用于估计因果效应的条件，并描述了如何使用Durbin-wu-hausman规范测试来测试数据中提出的估计器的一致性。从经验上讲，我们证明了因果推理模型在具有已知地面真相和从Wikipedia收集的大规模观察性关系时间序列数据集的合成数据上的有效性。
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.
