利用专家抽象图谱，推断时间序列微观因果方向。
Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions
- 基于摘要因果图和忠实分布，给出微观边定向的理论条件。
- 即使宏观存在环路或双向边，仍可保证微观边可定向。
- 适合需融合专家知识进行复杂时间系统因果发现的研究者。
理解时间序列变量间的因果关系是时间序列分析的核心挑战，尤其当完整因果结构未知时。尽管无法完全确定完整因果结构，专家常能提供高层次抽象的因果图（即摘要因果图），捕捉不同时间序列间的主要因果关系，同时忽略微观细节。本文提出在已知摘要因果图、且假设观测数据服从相对于真实未知图的忠实且因果充分分布的前提下，确保微观层面变量间边可定向的条件。结果为在存在宏观环路或双向边的情况下，仍能对微观边实现定向提供了理论保障。这些发现为利用摘要因果图指导复杂时间系统的因果发现提供了实践指导，并凸显了融入专家知识以提升观测时间序列因果推断能力的价值。
原文摘要 · Abstract (English)
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.
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