Understanding causal relations between temporal variables is a central challenge in time series analysis, particularly when the full causal structure is unknown. Even when the full causal structure cannot be fully specified, experts often succeed in providing a high-level abstraction of the causal graph, known as a summary causal graph, which captures the main causal relations between different time series while abstracting away micro-level details. In this work, we present conditions that guarantee the orientability of micro-level edges between temporal variables given the background knowledge encoded in a summary causal graph and assuming having access to a faithful and causally sufficient distribution with respect to the true unknown graph. Our results provide theoretical guarantees for edge orientation at the micro-level, even in the presence of cycles or bidirected edges at the macro-level. These findings offer practical guidance for leveraging SCGs to inform causal discovery in complex temporal systems and highlight the value of incorporating expert knowledge to improve causal inference from observational time series data.

翻译：理解时间变量间的因果关系是时间序列分析的核心挑战，尤其在完整因果结构未知的情况下。即使无法完全确定完整的因果结构，专家通常能够提供因果图的高层抽象表示，即摘要因果图，它捕捉不同时间序列间的主要因果关系，同时抽象掉微观层面的细节。本文提出了在给定摘要因果图编码的背景知识、并假设可获得关于真实未知图的忠实且因果充分的分布条件下，保证时间变量间微观层面边可定向性的条件。我们的结果为微观层面的边定向提供了理论保证，即使在宏观层面存在环或双向边的情况下依然适用。这些发现为利用摘要因果图指导复杂时间系统中的因果发现提供了实用指引，并凸显了结合专家知识以改进从观测时间序列数据中进行因果推断的价值。